SUNY at Stony Brook

NSF Awards · FY 2026 · 2026-07

This project will develop methods to control the selectivity of multifunctionalization reactions, which would enable production of complex valuable chemicals from simple inexpensive starting materials. Multifunctionalization reactions involve multiple bond-breaking and making steps in a single reaction and can be used to rapidly increase the molecular complexity, and value, of abundant hydrocarbon feedstocks. However, poor selectivity in these transformations often leads to an unusable mixture of products. The research team under Dr. Bruch will investigate how specifically designed substrates can be used to address this challenge and develop clear rules for broad implementation. The project will train undergraduate and graduate students to investigate the molecular details of organometallic catalysis and the design of new catalytic reactions to facilitate the synthesis of products with pharmaceutical, materials, and energy applications. Furthermore, the project will support (i) the development of a summer workshop series to improve the technical, software, and data analysis skills of undergraduate and graduate students and (ii) a hybrid workshop-conference for undergraduates that provides training in science communication with general and technical audiences. This project will examine and evaluate how remote leaving groups can be used to control the regioselective multifunctionalization of unsaturated hydrocarbons. The key premise of this work is that the initial functionalization can be used to activate a remote leaving group, thereby creating a trigger for controlling the multifunctionalization reaction cascade. Initial targets will focus on the deaminative multifunctionalization of alkenes, alkynes, and allenes before targeting the activation of remote C–O and C–F bonds. These studies will be coupled with detailed mechanistic and kinetic investigations in order to identify different modes of control and enable the divergent synthesis of valuable products from cheap, unified feedstocks. Overall, the project will advance our fundamental understanding of control in multifunctionalization reactions and afford access to products with unique substitution patterns of interest to commodity, materials, and pharmaceutical 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 2026 · 2026-07

This project is developing catalysts based on earth-abundant titanium to promote value-adding chemical transformations of organic molecules driven by light. Photoactive metal catalysts are commonly used for organic synthesis. A key step in these reactions is the movement of electrons between the catalyst and the substrate. An alternative approach relying on catalyst-substrate interactions to turn on photoactivity will allow for new types of reactions and selectivities to be realized. Furthermore, while the former type of photocatalyst typically relies on rare precious metals, titanium is one of the most abundant metals in the earth's crust. Through rational design of the ligand environment on titanium, these catalysts can be fine-tuned to enable a multitude of valuable reactions. In addition, the project will support the expansion of an annual regional symposium focused on photochemistry to bring together researchers and students from various types of institutions to share science and provides opportunities to seed collaborations and facilitate the development of a near-peer mentoring network among students at local primarily undergraduate institutions and major research universities. Finally, the project will also support the development of a graduate-level course on photochemistry which crosses disciplinary silos to engender deeper understanding of this essential topic in modern chemical research. This project will rationally develop titanium-based catalysts capable of ligand-to-metal charge-transfer (LMCT) excitation to drive the generation and control of high-energy heteroatom centered-radicals. Through rational design of the ligand set on Ti, photophysical, photochemical, and redox properties can be fine-tuned to enable reactions transiting through oxidizing chlorine and alkoxyl radicals for carbon–hydrogen and carbon–carbon/carbon-oxygen bond functionalizations, respectively. This project will establish Ti-LMCT as a versatile platform for photocatalysis operating with a tunably wide redox window, with particular reaction pathways selected through catalytic control. Platforms will be developed for: (1) C–H bond functionalization through hydrogen atom-transfer (HAT) using novel, bench-stable bipyridinyl titanium chloride complexes; (2) alcohol C–C bond functionalization for enabling molecular derivatizations through catalyst-controlled, non-intrinsic selectivity paradigms; and (3) cascade activation by photochemically-driven Ti radical redox to functionalize C–O and C–C bonds in sequence for nominally inert bond activation. The proposed work will not only lead to the development of empowering chemical transformations driven by catalysts based on earth-abundant titanium, but will also lead to deeper understanding of strategies to leverage early transition metal photochemistry for catalysis and exert control over highly reactive radical species. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

Quantum information science (QIS) is rapidly advancing under the National Quantum Initiative with transformative progress in quantum computing, quantum sensing, and quantum networking. While these technologies are poised to revolutionize multiple sectors, their potential impact on the Geosciences remains largely unexplored in a coordinated manner. This award will support the Quantum Technologies for the Geosciences workshop, which will examine how Quantum Information Science (QIS) could transform Geoscience research by offering new ways to address some of the complex computational and observational challenges in Earth system science. By identifying pathways to integrate QIS into geoscience research, this workshop supports the National Quantum Initiative and acceleration of quantum research and development for the economic and national security of the United States. Quantum Technologies for the Geosciences workshop will bring together experts in QIS and Geosciences to identify high impact opportunities at the intersection of these fields. Experts in quantum computing, quantum sensing, atmospheric science, oceanography, geophysics, and Earth system modeling will assess the transformative potential of quantum technologies for the geosciences. By connecting researchers from traditionally separate fields, the workshop aims to catalyze new interdisciplinary collaborations and identify research areas grounded in both quantum technological feasibility and high-impact geoscientific questions. The resulting roadmap will help identify research priorities at the intersection of QIS and Geosciences, aligning foundational advances in quantum technology with pressing challenges in Earth system science. Outcomes from the workshop will be broadly disseminated through a publicly available workshop report. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-04

This NSF CAREER project aims to establish a new paradigm of “fully quantum” computing for power grid analytics. The project will bring transformative changes by redefining how linear quantum operators can be harnessed to address nonlinear power grid analysis, thereby opening a new pathway for end-to-end quantum computing technologies capable of tackling critical power system computational tasks. This will be achieved by fusing quantum computing with analytic continuation to reformulate nonlinear power grid equations into an equivalent linear structure that can be solved directly and entirely in the quantum space, thus eliminating reliance on classical iterative processes. The intellectual merit of the project includes the development of fundamental theory and novel algorithms for fully quantum grid analytics for AC power flow and dynamic simulation, along with their associated contingency screening. The broader impacts of the project include promoting preparation of quantum-ready power engineers who will accelerate the adoption of quantum information science and technology (QIST) in power systems. This will be achieved through 1) developing a fully quantum analytics toolbox to address industry needs and broaden community engagement; 2) delivering impactful quantum-grid-focused educational resources spanning undergraduate, graduate, and K–12 programs; and 3) collaborating with industry partners to transition research outcomes into practical workforce training materials. As modern power grids have become more complex due to rising demand, expanding infrastructure, and dynamic operating conditions, existing computational tools face increasing challenges in analyzing large-scale nonlinear power grid behavior. Breakthroughs in quantum computing promise exceptional scalability, parallelism, and computational efficiency. However, the fundamental mismatch between the linear nature of quantum operators and the nonlinear characteristics of power grids has restricted existing quantum-enabled grid analytics to hybrid quantum–classical frameworks, hindering the realization of true quantum advantage. This project addresses this barrier by introducing a fully quantum framework that transforms nonlinear power grid equations into higher-dimensional analytic continuation structures compatible with linear quantum computation. Quantum tensors will be employed to process massive scenarios and simulation steps in a single quantum execution. Quantum Krylov subspace methods will further ensure practical scalability to large grid dimensions and across numerous operating scenarios. The proposed methods will be validated on real-world power system use cases provided by industry partners using today’s real quantum 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 · 2026-01

Grasses are beneficial to human society by creating habitat for bees, and other pollinators, that ensure crops produce fruit and seeds, improve the quality of water, trap carbon, and provide food for animals upon which people rely for nutrition. However, grasses are a large group of over 11,000 species, which cover ~50% of the earth’s surface, and there are differences in their ability to perform beneficial ecological functions. For example, they can differ in the time of year they grow (also called phenology), how fast they grow, and their ability to tolerate and survive droughts. Importantly, there are often trade-offs between these characteristics, such that species that only grow in the spring may not be very tolerant of drought, and species that only grow during the summer generally do not grow very fast. Therefore, environmental changes during different seasons may prevent some species from thriving in their current locations, altering the ecosystem services they provide. To effectively manage resilient grasslands for the future, we need better information on the phenology, growth rates, and drought tolerance of a broader range of grass species. Most projects that measure these characteristics have focused on trees, leaving major gaps in our understanding of these traits in grasses. Using novel techniques to observe processes occurring inside the leaf and new mapping methods, our project will provide critical information about plant traits and tradeoffs in different environments to help predict how grass distributions will respond to changing weather patterns and environmental conditions. Changes to plant communities are continually occurring as plants disappear, appear, and re-arrange in ecosystems across the globe as rising temperatures and changing precipitation patterns reduce the available water for plant growth. Plant responses to these dynamic conditions dictate whether a species can persist in a region or must shift distributionally. Modern approaches to modeling species distributions rarely include the mechanistic underpinnings of organismal responses but, instead, rely on bivariate relationships between individual traits and annual summaries of abiotic conditions. This approach ignores the fact that networks of traits, rather than any single trait, generate different drought-coping strategies and that drastic differences in grass phenology decouples plant growth conditions from annual summaries of abiotic conditions. To improve predictions of future species distributions and inform restoration projects of ideal seed-mixes, the overall objective of our study is to improve the accuracy of species distribution models through a better understanding of grass species resilience by including trait networks and growth phenology. Using a set of species that spans the entire grass family, the investigators will identify mechanistic trait networks leading to different drought-coping strategies, including mechanisms leading to embolism formation, a key drought-coping trait rarely studied in grasses. Integrating these key traits will provide information on species responses and distribution shifts and the experimental design will also provide information on how these traits may evolve independently or in unison within the grass family. 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

Ecological degradation driven by human behavior and the resultant disruptions to sensory processing is a major focus of conservation neuroscience. The impacts of odor pollution have come under increasing scrutiny in recent decades. Agrochemical scent pollution has been found to disrupt bumblebee behavior, a particularly alarming finding in light of their critical role as pollinators in agricultural and natural ecosystems. These findings imply that neural processing of floral odors is impacted by odor pollution. One barrier for understanding these impacts of agrochemicals on bumblebee foraging behavior is that there are no concrete computational structures for representing and exploring odor perception, as current methods are statistical in nature. This means pollution cannot be easily measured, or quantified; which makes it challenging to develop agricultural recommendations. This project is building upon earlier work that established a quantification mechanism for complex odors (i.e. odors made up of many molecules) to establish an algorithm for representing bumblebee odor perception. The established “Compounds with Borders” (CWB) method allows the difference between any two odors to be represented as an angular distance, and has been used to establish a ‘safe zone’ of odor pollution for complex odors: polluted-odors within a 20-30 degree range are generalized. Next steps include expanding CWB into a more comprehensive geometry that can accurately account for simpler odors as well. This work will be performed at a ‘primarily undergraduate institution’, incorporating valuable research experiences for the next generation of STEM professionals. Given the neurophysiological organization of odor processing, the relative importance of molecular-identity versus -feature of odorants is linked to stimulus complexity, with encoding of simpler odors correlating with identity and more complex odors correlating with features. “Compounds with Borders” is particularly effective for complex blends because it quantifies the amount of sensory energy that is distributed across molecular features in an odor using a Euclidean approach where ‘dimensions’ in odor space represent those features. The next hurdle is to expand the geometry of this odor-space to incorporate molecular identity. This is not logistically tractable without data that delineate what level of odor complexity shifts the primary-processing output from ‘identity’ to ‘feature’ correlation. This project aims to develop a more comprehensive geometry using associative odor learning paradigms combined with recording neural activity at both the input (antennae) and output (antennal lobe tracts) from primary olfactory processing. Gas Chromatography-Electroantennographic recordings will establish a species specific odor-salience across molecular identities and features. Spike-resolved multi-unit recordings from the antennal lobe tracts will assay odor information as it moves forward to integration and action centers. Comparing the point at which odor responses shift from correlating with molecular identity to molecular feature at a neurophysiological and behavioral level will inform an expanded geometry that utilizes stimulus complexity to shape dimensional weighting. Thus, this work aims to establish a novel paradigm for vertical integration of neural encoding through behavior in olfaction. 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: Chiral molecule sensing plays an important role in many areas such as pharmaceutical industry, biomedical diagnostics, and food analysis. It is challenging to accurately quantify the chirality of chiral medium due to the weak intrinsic circular dichroism signal of chiral molecules. Recently, chiral metasurfaces have been used to amplify the circular dichroism signal and improve the sensitivity in circular dichroism spectroscopy for the vibrational transitions of chiral molecules. However, the detection sensitivity is quite limited by the chiral metasurface design with certain structural geometries and optical properties. In this project, a new type of 2D chiral fingerprint metasensors with high sensitivity based on ultrathin 2D material chiral metasurfaces will be rapidly designed through a machine learning framework and demonstrated for the detection and identification of various kinds of chiral molecules with high sensing performance. This research will benefit many biomedical and photonic applications in point-of-care healthcare, food analysis, environment monitoring, quantum spectroscopy, and quantum communication. This project also includes educational activities for training graduate students, recruiting students and promoting broad participation, and mentoring high school students through outreach activities. Technical description: Chiral metasurfaces have been utilized to enhance the circular dichroism signal and improve the detection sensitivity for the vibrational transitions of chiral molecules. However, the detection sensitivity is quite limited by the chiral metasurface design with certain structural geometries and optical properties, which are insufficient to cover the whole design space to achieve the optimal sensitivity in chiral molecule sensing. The goal of this project is to study a new type of 2D chiral fingerprint metasensors based on ultrathin 2D material chiral metasurfaces which will be rapidly designed through a machine learning framework and demonstrated for the detection and identification of the mid-infrared vibrational fingerprints of chiral molecules with high sensitivity and selectivity. In this project, a new machine learning algorithmic methodology will be developed for rapid and precise inverse design of 2D chiral fingerprint metasensors with the maximized sensitivity. The underlying mechanism of high sensitivity in 2D chiral fingerprint metasensors will be revealed. Nanofabrication processes will be developed to fabricate the designed 2D material chiral metasurfaces. The 2D chiral fingerprint metasensors will be characterized for demonstrating the detection and identification of various kinds of chiral molecules with high sensitivity and selectivity. The project will advance the rapid design and integration of future 2D material-based photonic and optoelectronic metadevices. 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 United States is facing a sharp increase in electricity demand, driven by the rapid growth of AI data centers and a resurgence in domestic manufacturing. Meeting this demand while sustaining U.S. leadership in innovation requires the reliable integration of all available electric generation sources into the national grid. Electromagnetic transients (EMT) studies are essential to this effort, as they capture system dynamics by continuously tracking the evolution of grid states. High-fidelity EMT simulations, mandated by regulatory and planning bodies, are critical for ensuring grid reliability and secure energy integration. Three challenges, however, make EMT studies prohibitively costly and difficult: 1) EMT simulations involve polynomial-time matrix computations at each step; 2) Capturing fast inverter-induced transients requires extremely small time steps, resulting in a vast number of calculations; and 3) Time- and frequency-domain contingency screening becomes prohibitively difficult when faced with massive contingencies and operational scenarios. Building on the PIs’ pioneering work on quantum grid analytics, this project aims to establish a scalable quantum EMT (QEMT) framework with ultra-fast screening capabilities in both time and frequency domains. The broader impacts of this project include: First, QEMT will be adopted by major independent system operators (ISOs) and power utilities, enabling the accelerated and reliable deployment of gigawatts of added generation and the secure interconnection of massive loads. Second, the project will contribute significantly to both Power Engineering and Quantum Information Science by advancing quantum computing and data processing techniques and establishing a living laboratory for quantum-enabled power grids. Third, with strong support from the State of New York, this project will also serve as a cornerstone for quantum-energy education. It will foster the development, reskilling, and upskilling of a quantum-ready workforce through training programs spanning K–12, university, and professional education. The overarching goal is to develop a domain-specific QEMT framework with unparalleled scalability, efficiency, and noise resilience—surpassing both classical EMT solvers and general-purpose quantum algorithms. The intellectual merit of this project includes: First, it will Integrate quantum tensors and quantum singular value transformation (QSVT) to develop an implicit parallel QEMT formulation capable of capturing full transient trajectories without step-by-step numerical integration. Second, a novel quantum architecture is devised for scalable contingency screening and frequency scanning using ultrafast circuit reconstruction. Third, it will enable scalable encoding/decoding between QEMT simulations and classical grid data. Finally, it will validate QEMT’s capabilities in grid planning, operations, and interconnection studies on real-world systems, including CIGRE’s DC grid and large-scale networks from industry partners such as an ISO and a major utility. 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

Quantum sensors capitalize on the strong sensitivity of quantum systems to external disturbances to measure various physical phenomena with extreme precision. Quantum sensing is a rapidly emerging field with many applications, including detecting gravitational and magnetic fields, biological measurement, imaging, etc. The full potential of quantum sensing is realized by using a distributed network of quantum sensors to estimate physical phenomena; in particular, when a network of quantum sensors allows the sensors to be in an entangled (correlated) state, its precision is further improved. There has been recent work using multiple quantum sensors, but the use of a distributed network of quantum sensors working collaboratively to estimate complex physical phenomena, as in many classical sensor network applications, has remained largely unexplored. This proposal seeks to fill this gap and investigate many scientific challenges that arise in developing efficient sensing protocols for a quantum sensor network (QSN). In addition, the project helps develop the workforce in this emerging quantum sensing and communication field by designing and offering educational programs targeting a wide variety of students ranging from those in high school to those in graduate school and beyond. The goal of this project is to tackle the main challenges and problems that arise in building QSNs. The research work consists of the following thrusts: (i) Initial State and Measurement Optimization. The initial state of the quantum sensing system can strongly affect the estimation error of the sensed parameter. Thus, the project will investigate the optimization problem of determining the optimal initial state and global measurement that minimizes the estimation error. (ii) Event Localization Schemes via QSNs. The project will design efficient schemes for event localization using QSNs. (iii) Distribution of Quantum Circuits in QSNs. The project will develop techniques for the distributed implementation of quantum sensing circuits in QSNs, with an objective to minimize aggregate quantum error or execution latency of distributed circuits. (iv) Declarative Framework for Specifying QSN Protocols. The project plans to develop a programming framework for specifying, evaluating, and reasoning over QSN protocols. (v) QSN Simulator, System Evaluation, and QSN Testbeds. The project will develop a QSN simulator that can be used to simulate general QSN applications. In addition, the developed techniques will be evaluated over three platforms: large-scale simulations over the QSN simulator, moderate-scale simulations over a cloud quantum computer, and small-scale experiments on two QSN testbeds. 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 ExLENT Explorations Tack project aims to serve the national interest by launching a workforce development initiative to expand access to high skilled careers in semiconductors and advanced manufacturing. Led by Stony Brook University (SBU) in collaboration with Brookhaven National Laboratory (BNL), SUNY community colleges, and industry partners, the initiative addresses the critical national need for a technically prepared workforce. It focuses on learners from non-STEM backgrounds, with a goal of serving up to 75 participants. Through immersive training in worldclass clean room nanofabrication facilities and direct connections to established hiring pathways in industry and national laboratories, participants gain practical skills and clear entry points into the field. Importantly, the project supports U.S. technological and economic leadership by strengthening the domestic workforce in advanced manufacturing and other strategic emerging technologies. The project’s primary goal centers on ensuring participants gain and retain essential and preferred job-specific qualifications required for technician and engineering roles in areas such as mechatronics, maintenance, and chemical and material synthesis. These qualifications include experience working in cleanroom environments while using the appropriate garments and equipment and demonstrated ability to operate in ways that uphold Environmental, Health, Safety & Security (EHS) requirements. To achieve this goal the project offers two structured tracks: 1) Explorer, with three-month traineeships and 2) Developer, with one-year traineeships, each combining technical training and professional development to support career readiness. Participants build technical and professional competencies through hands-on work at BNL’s Center for Functional Nanomaterials, guided mentorship, career coaching through the SBU Career Center, and engagement with industry partners. The project leverages a strategic cross-sector partnership governed by an advisory board that ensures broad stakeholder representation and continuous project improvement. Evaluation and assessment efforts generate new evidence on which strategies most effectively support skill acquisition and long-term competency retention in STEM careers. In particular, the project produces actionable insights, metrics, and guidance on the effectiveness of mentor training and cohort-based engagement. Findings are shared with advisory committee members, presented at national conferences, and developed into research publications on experiential learning models for semiconductor workforce development. This effort advances an innovative, collaborative model for preparing the next generation of STEM professionals and offers a scalable approach for other regions across the country. The NSF ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and their access to career pathways in emerging technology fields. 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 intrinsic high strength, light weight, and heat, corrosion, and irradiation resistance of ceramics positions them as nearly ideal structural materials. However, their inability to resist the growth of cracks causes any tiny flaw to grow into a catastrophically large crack; this renders ceramics brittle and impractical as structural materials for automotive, energy, aerospace, and defense applications, to name a few examples. The overarching goal of this Designing Materials to Revolutionize and Engineer our Future (DMREF) project is to transform the fracture resistance of ceramics by introducing heterogeneous metallic features across multiple length scales into the ceramic material. The fundamental challenges to be overcome in the project are: (1) how can heterogeneous ceramics be computationally designed when the space of possible designs is massive? and (2) how can such engineered ceramics be manufactured? These challenges will be overcome by leveraging recent advances in machine learning for material modeling in conjunction with advanced low-temperature ceramic processing techniques. The revolutionary new class of materials designed through the project can be directly implemented in commercial applications, such as satellite structures, low-wear medical devices, armor, and hypersonic vehicles. Insights gained on the design, processing, and fracture of heterogeneous ceramics will drive future innovations enabling next-generation structural materials. Despite many decades of development of advanced ceramics, fracture toughness values have remained consistently below about 15 MPa-m0.5, a factor of three less than typical structural metals. This DMREF project aims to transform the toughness of ceramics through the introduction of hierarchically heterogeneous metallic interphases that will drive toughening via crack multiplication and deflection ahead of an advancing crack tip. The structure of the interphases and their distribution in the microstructure will be designed using molecular dynamics and finite element simulations of fracture. To accelerate computational materials design, finite-element-based physics-informed neural networks will be employed after training on cohesive surface and phase field finite element fracture models. The computationally designed ceramic microstructures will be produced using non-conformal coating of ceramic powders in conjunction with low-temperature sintering enabled by demonstrated doping technologies to avoid melting of the metallic interphases. R-curves will be used to quantify fracture response via macro and micro-scale mechanical testing. After establishing all necessary modeling, processing, and mechanical testing capabilities, a closed-loop design cycle will be executed utilizing the machine-learning accelerated model for microstructure design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

This I-Corps project focuses on the development of a novel, shelf-stable skin scaffold designed to accelerate healing in complex wounds. Such wounds include injuries that occur in inflamed or infected tissue environments, such as diabetic ulcers, radiation burns, traumatic injuries, and chronic wounds. Current treatment options often fail in these settings due to poor vascular integration, high infection risk, and limited durability. This scaffold is intended to reduce hospitalizations, enhance healing outcomes, and decrease long-term healthcare costs. Designed for rapid deployment and storage without refrigeration, the solution has the potential to improve wound care in civilian, military, and resource-limited environments. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This technology is based on the development of a pre-vascularized, acellular dermal scaffold fabricated using three-dimensional bioprinting and supercritical carbon dioxide decellularization. The construct incorporates perfusable microvascular networks and retains native extracellular matrix components to support host integration. The solution is designed to enable rapid vascularization, modulate immune response, and provide mechanical robustness in compromised tissue. By eliminating cold-chain requirements and enabling off-the-shelf usability, the scaffold represents a scalable platform for next-generation wound healing applications in clinical, trauma, and austere care settings. 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 CSSI project is a multi-university collaboration between Tennessee Technological University, the University of Tennessee, Knoxville, Stony Brook University, and the Illinois Institute of Technology. This project improves how massively parallel computers run large-scale artificial intelligence (AI) applications by enhancing the Message Passing Interface (MPI), a widely used standard for coordinating work across many computers in parallel programs. Currently, the enabling data-transfer software used in AI, for communication between computers enhanced by Graphical Processing Units (GPUs), are often proprietary and/or limited in scope; they cannot be expanded or enhanced by an open community. That situation restricts innovation, making it harder for scientists to collaborate and enhance their science output on limited computer resources, while also creating dependency on a few vendors. By contrast, this project builds on and advances Open MPI, a major open-source implementation of MPI with a long history of broad impact, to make it more efficient, flexible, and better suited for modern AI tasks. In addition to improving the Open MPI implementation, MPI4AI aims at standardizing extensions to MPI so all implementations and users of MPI will benefit from this project's outcome. MPI4AI introduces key improvements to Open MPI, including native support for GPU communication, enhanced collective (group) communication operations including those that are AI-algorithm specific, compute stream integration, and optimized data movement. Specifically, these advances target performance bottlenecks in three AI patterns: neural architecture search with transfer learning, key-value prefix caching in large language model inference, and large-scale data-parallel training. The project improves resilience and malleability through fault-tolerant mechanisms, enabling AI applications to adapt dynamically to system changes and to use resources more efficiently. By forwarding these enhancements toward adoption in the upcoming MPI-5 and MPI-6 standards, the project ensures long-term impact across both academic research and industrial AI workflows. These contributions will lower the cost of running large AI workloads and broaden access to scalable AI infrastructure. MPI4AI's capabilities will enable researchers exploring new modalities of AI computation to express their algorithms and code efficiently and more effectively as compared to existing solutions that work within the confines of current MPI features and vendor-specific message-passing libraries. Underlying improvements devised for Open MPI will also be broadly beneficial to other use cases and users of this parallel programming system. Overall, key strengths of this effort are a strong commitment to standardization and emphasis on performance-portability across various hardware platforms with particular focus on AI-enablement. 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 Catalysis program in the Division of Chemistry, Professor Gonghu Li of the University of New Hampshire, Professor Jier Huang of Boston College and Professor Anatoly Frenkel of Stony Brook University are studying key characteristics of catalysts that promote efficient conversion of carbon dioxide to energy-rich fuels using light. The catalysts will be prepared by placing isolated metal ions on a polymer that absorbs sunlight and generates electrons that convert carbon dioxide to fuels. The catalyst structures will be investigated using advanced spectroscopic techniques and machine learning-assisted data analysis. This research will contribute to the development of innovative catalysts for recycling carbon dioxide. With a strong focus on fundamental catalysis research, this project also provides a versatile platform for training students in the STEM fields. With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Gonghu Li of the University of New Hampshire, Professor Jier Huang of Boston College and Professor Anatoly Frenkel of Stony Brook University are studying photocatalysts featuring atomically dispersed surface metal sites. The researchers with complementary expertise collaborate to investigate photocatalytic activity descriptors of single cobalt sites on graphitic carbon nitride. A series of photocatalysts with tunable charge separation and transfer properties will be synthesized. The correlation of structural/electronic and charge transfer descriptors with photocatalytic performance will be systematically examined by using a suite of advanced, real-time spectroscopic techniques including in situ/operando X-ray absorption fine structure, time resolved optical transient absorption and X-ray transient absorption spectroscopies. Machine learning will be employed for descriptor extraction from spectroscopies and their relationships with photocatalytic activity. Results obtained from this work will lead to the discovery of key descriptors for photocatalytic activities of well-defined metal sites on semiconductor support, and thus provide new insights on developing robust and economically sustainable photocatalysts. This collaborative project focuses on fundamental research in catalysis, and provides a versatile platform for training students in the STEM fields. 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 grant supports research that looks to advance the knowledge of how fluid-coupled granular media behave. Because granular media are ubiquitous in natural systems (such as soils in riverbeds and landslides) and infrastructure (such as concrete and ballast), this research will promote both the progress of science and engineering, as well as advance national prosperity. When a load is applied to a granular medium, such load is transmitted via a network of forces among grains that are in contact. This network of forces, or force chains, is the ultimate determinant of how granular media behave under external loading (e.g., compression and shear) and under fluid injection and withdrawal. Understanding the spatial structure and temporal evolution of force chains constitutes a fundamental goal of granular mechanics. However, the current knowledge of granular media is limited by the experimental observations on force chains, which are either on two-dimensional packs or on three-dimensional packs with limited grain shapes or loading conditions. The coupling between the solids and fluids in granular media adds yet additional complexity for observations and modeling. This award supports fundamental research looking to advance experimental techniques and the associated theory for fluid-coupled granular media, enabling the observation of the transmission of external loads on both the single-grain scale and the granular pack scale. The outcomes of this research intend to provide new knowledge of the organization of contact forces in fluid-coupled granular media at the grain scale, and help predict their behavior in natural systems like landslides and earthquakes, as well as engineering applications like construction materials, infrastructure and robotics. The outcomes of the research will be integrated into undergraduate and graduate courses and multiple well-organized outreach activities, such as the Simons STEM Scholars program, the Simons Summer Research Program, and Engineering Academy for grade 6-12 students, with an expectation to engage a broad group of students, thus positively impacting engineering education in the US. This research looks to advance the fundamental understanding of the mechanical behavior of granular media by developing innovative experimental and theoretical techniques that will enable accessing, quantitatively, the stress-tensor field, and associated force chains, in 3D granular packs of round and angular particles, under various load conditions and fluid-coupling scenarios. This research seeks to develop the experimental apparatus and associated theory for the tomographic reconstruction of stress tensors in fluid-coupled granular media under external loads in 3D, based on interference optical projection tomography. This new method intends to advance the understanding of the tensor nature of effective stress on the grain scale, which results from the normal and tangential contact forces between particles of various shapes and moduli, as well as elucidate the spatial structure and temporal evolution of force chains in 3D packs of angular and round particles under various stress conditions and fluid-coupling scenarios. 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

Three-body recombination reactions, requiring three reactants to form the reaction product, are key to a multitude of relevant physical processes, spanning more than four orders of magnitude in temperature, from cryogenic environments to plasmas. However, despite their relevance, there is no general theoretical framework to deal with them. This project aims to develop a comprehensive theoretical framework for three-body recombination reactions, including atoms, molecules, and ions as reactants, with applications in cold chemistry, atmospheric chemistry (ozone formation), and plasma physics. Additionally, this project will focus on a new course designed to orient new students across departments on what a Physics degree can offer in the professional world. This project has three main objectives: (1) to develop a theoretical framework for three-body recombination reactions involving partners with internal degrees of freedom; (2) to apply semi-classical theory to three-body recombination reactions: hybrid ion-atom traps and plasma physics; and (3) to understand ozone formation in the stratosphere. To achieve these objectives, a novel semi-classical theory for three-body recombination reactions will be developed, based on a classical trajectory approach in hyperspherical coordinates. The results will be benchmarked against existing (but unexplained) experimental data in several systems of interest. Furthermore, the underlying potential energy surfaces required for evaluating the reaction dynamics of each system under consideration will be determined using a hybrid machine learning-ab initio approach, thus obtaining a more reliable and efficient description of the interatomic energy at a lower computational cost. 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

A research collaboration between the State University of New York at Stony Brook and the American Museum of Natural History has been operating the Condor Array Telescope at a very dark astronomical site in the southwest corner of New Mexico since 2021. With renewed NSF support, the team will add two new telescopes to the instrument, significantly enhancing its capabilities. They will continue to operate Condor to obtain, analyze, and interpret a variety of observations spanning the entire northern sky, focusing on several important science topics to which the instruments can make particularly significant contributions. In addition, the team will produce a one-hour-long video documentary featuring Condor New Mexico and the rich cultural history of the North American civilizations in the region. This film will explore the interaction between scientific inquiry and cultural history, with a particular emphasis on the Apache people, whose ancestors have lived in the region for centuries. The team will also incorporate their survey images into a Hayden Planetarium space show tentatively titled “Multi-Messenger Astrophysics.” Condor combines six off-the-shelf refracting telescopes with six off-the-shelf CMOS cameras. It is optimized for low-surface-brightness sensitivity, wide field of view, and rapid time resolution. The instruments will detect and study large portions of the extremely faint and extremely distant filaments of the “cosmic web” of intergalactic gas that stretches between the galaxies, seeking to understand how the largest-scale structures of the Universe form and evolve over time; image the sky in eight of the most important emission lines of astrophysics, making the resulting survey immediately available as a community resource; observe the rapidly-evolving afterglows of gamma-ray bursts within the first seconds following their discovery, providing a direct window into the mechanisms driving the most energetic explosions known; and monitor the entire stellar bulge of the Milky Way at a rapid 20-second cadence, searching for low-mass planets, moons, asteroids, and more toward the center of our Galaxy. 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 plant and animal responses to environmental change across space and time remains a longstanding question in biology, with pressing relevance in today’s rapidly changing world. The fossil record provides key information about the influence of climate and landscape on species ecology and evolutionary history; however, parts of the fossil record remain vastly understudied. Small mammals, such as rodents, are challenging to study in the fossil record, yet as a diverse group of early responders to shifts in environmental conditions hold untapped potential for understanding these relationships today and in the past. This research will generate and assess multiple lines of evidence to illuminate long-term responses to environmental change in small mammals across two systems, the Basin and Range Province of western North America and the East Africa Rift in Kenya, over the last 23 million years. Work in the US and Kenya will additionally foster collaboration between student researchers and promote capacity building for national and international researchers in paleontology. Research and educational aims will be unified through a training program that includes workshops and activities centered on 1) advanced approaches in analytical paleobiology, 2) feasible solutions for data sharing, and 3) scientific communication. In collaboration with the Turkana Basin Institute, educational objectives also include a redesigned field course with embedded research in Vertebrate Paleontology and the development of educational materials for local schools in Turkana Basin, Kenya. This CAREER proposal aims to develop novel multi-proxy trait data from the small-mammal fossil record to capture dietary ecology, habitat use, and ecological structure across a hierarchy of taxonomic, spatial, and temporal scales. Research will unfold across the fossil record of two continental systems–the Basin and Range Province of western North America and the East Africa Rift in Kenya–providing a comparative framework for evaluating the roles of global and regional climate change, the expansion of C4 grasslands, and tectonic regime on the eco-evolutionary history of small mammals during the Miocene (23-5 Ma), a period known for the assembly of modern biota. To reconstruct the paleoecology of small mammals (rodents and lagomorphs), this research will develop a multi-proxy toolkit that includes dental metrics, dental topographic analysis from high-resolution microCT scans, and stable isotopic composition using laser ablation methods for sampling sub-millimeter teeth. Trait-based measurements of ecological structure across spatial scales will be evaluated against local-scale paleoenvironmental data and regional-scale tectonic and climate models to test process-level explanations for the observed patterns in the fossil record. Educational objectives include the development of field-, collections-, and lab-based educational and research experiences for students and early-career scientists. The project will additionally foster and support a cohort of US and Kenyan scholars to produce collaborative research, implement feasible solutions to challenges in data sharing, participate in technical training, and generate educational resources for local communities in Kenya. 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 investigates an innovative strategy for storing carbon dioxide in a way that potentially is both effective and scalable. This research explores the potential of Azolla, a fast-growing aquatic fern, to support long-term carbon storage in soils. Unique among plants for its ability to fertilize itself by fixing nitrogen, Azolla grows rapidly and could serve as a renewable source of organic material that improves soil health while drawing down carbon dioxide from the atmosphere. The project investigates how Azolla-based soil amendments can contribute to climate mitigation by storing carbon, while also examining how these amendments affect soil life, including plants, bacteria, and earthworms. By focusing on a nature-based solution with minimal input requirements, the research aligns with sustainability goals. Additionally, by potentially integrating nutrient recovery from sources such as wastewater, it also supports the principles of a circular economy. The activity will provide significant educational opportunities through a course-based undergraduate research experience, engaging nearly 120 undergraduate students, graduate students, and a postdoctoral fellow. This will strengthen the science, technology, engineering, and mathematics workforce and promote public engagement with climate and environmental science. The technical goal of the research is to evaluate the use of Azolla as a soil amendment for carbon sequestration and to refine and test a new analytical model that predicts how Azolla grows, decomposes, and stores carbon in soil systems. The research team will compare three different Azolla-based amendments—raw Azolla, composted Azolla, and an Azolla-based biogel—across a series of greenhouse tests. These experiments will assess how much carbon is captured, how long it remains stored in the soil, and how each amendment affects soil health. The research will also determine how much phosphorus is required for Azolla cultivation to ensure that the approach remains sustainable and scalable. By combining controlled experiments with mathematical modeling, the project will provide new insights into the potential of fast-growing aquatic plants to serve as effective tools for biological drawdown and storage of atmospheric carbon dioxide. This work advances scientific understanding of ecosystem-based carbon management and contributes to the broader fields of climate resilience and soil ecology. 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 award is jointly supported by the Major Research Instrumentation (MRI) and the Division of Chemistry Research Instrumentation programs in response to the solicitation of proposals for equipment that promote the reduced consumption of helium initiated by the CHIPS and Science Act of 2022. Stony Brook University is acquiring a helium recovery system to capture and reliquefy helium from five nuclear magnetic resonance (NMR) spectrometers to support the research of Professors Robert Grubbs, Quinton Bruch, Jeffrey Lipshultz, Jeffrey Gustafson, and Benjamin Hsiao in the areas of organic, inorganic, polymer, medicinal, and materials chemistry as well as more than twenty additional groups with research focuses spanning geochemistry to chemical biology. NMR spectrometers rely on liquid helium to cool superconducting magnets and require regular deliveries to maintain operation. Recently, the global helium supply has experienced increased instability, endangering the use of NMR spectrometers for research and educational tasks. The helium recovery system addresses these challenges by creating a closed loop that captures helium boiloff from the spectrometers and generates new liquid helium through a reliquefication process. The five NMR spectrometers to be supported by the helium recovery system constitute the only spectrometers on campus and are critical to the research of numerous groups across multiple departments at Stony Brook and users from external companies, the training of researchers at all stages for workforce readiness (high school through postdoctoral), and the education of undergraduate students in formal coursework. The five NMR spectrometers supported by this equipment enable research projects including: (1) the discovery of new antifungals and anticancer medicines, (2) studies of catalytic reactions at gold nanoclusters, (3) investigating Martian surface waters for origins-of-life chemistry, (4) the development of PET diagnostics for bacterial infections, (5) investigations of the role of magnesium in Alzheimer’s disease, (6) measuring kinetics and thermodynamics of organometallic transformations, and (7) the development of nanocellulose-based technologies for water remediation. 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 early Paleogene (~66-48 million years ago) was an important time in Earth’s history: It immediately followed the mass extinction of all dinosaurs (except birds), many modern groups of mammals first appeared, and the Paleocene-Eocene Thermal Maximum (a significant climate event) occurred. Knowledge of these events is mostly based on a well-dated and characterized North American stratigraphic record; a global perspective on these events is missing. This project will apply modern, high-precision, age-dating techniques to the sedimentological and mammal fossil records of Mongolia. These methods will allow the building of a critical framework for comparing the North American and Asian fossil records across this important time interval. New physical and digital collections of fossils and a pop-up traveling exhibition on Paleogene mammal evolution and climate will be created. Developing a modern chronostratigraphy and paleoenvironment reconstruction for the highly fossiliferous Naran Bulak and Gashato Formations in Mongolia is the goal of this project. Four geochronological methods will be used, including magneto- and chemo-stratigraphy and Ar/Ar and U-Pb geochronology. Age and correlation data will be combined with careful sedimentological and paleoenvironmental analysis. These methods will be used to precisely constrain this important fauna and permit precise correlations with other parts of Asia and with the North America record. This geochronologic focus will be coupled with detailed sedimentologic analysis and stable isotope analysis of ancient soil and lake deposits to identify the PETM boundary by its signature global negative carbon isotope excursion. 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

Gram-negative bacteria are enclosed by two membranes with different compositions. The outer membrane normally has LPS, a lipid with sugars attached to it. We recently discovered that a model bacterium can survive without LPS if it produces CPG2, a type of ceramide lipid. This shows the importance of ceramides in bacterial biology and provides an opportunity to study alternative modes of outer membrane construction in bacteria. The proposed research is anticipated to discover how CPG2 is made and explain how the enzymes that make it work. This knowledge will be essential to discover the numerous roles of bacterial lipids in the various environments in which bacteria live, including the human body. Because many types of bacteria produce ceramides, this advance will contribute to answering important basic and translational research questions related to the production and function of lipids in all bacteria, including those causing disease. More broadly, this project will train students at the high school, undergraduate, and graduate levels. Specifically, these students will be trained in a multidisciplinary setting to develop skills in genetics, biochemistry, and structural biology. These skills are essential to develop the new generation of researchers, and a strong biotech workforce. Gram-negative bacteria are characterized by an envelope consisting of two membranes. The inner membrane is largely composed of phospholipids and functions similarly to the eukaryotic plasma membrane. By contrast, the bacterial outer membrane is unique in that it is asymmetric with an outer leaflet comprised mainly of the glycolipid lipopolysaccharide (LPS) which creates a permeability barrier against hydrophobic molecules including many antibiotics. Given its critical role in Gram-negative bacterial physiology, the genes involved in LPS synthesis are generally essential. Recently, our lab has characterized a mutant strain of Caulobacter crescentus that survives without LPS by synthesizing a novel anionic sphingolipid with a diphosphoglycerate headgroup (CPG2). Genetic analyses identified at least four genes required for CPG2 synthesis, but their specific enzymatic functions remain unclear. We have previously demonstrated that the first enzyme in this pathway is a ceramide kinase (CERK), distinct from the eukaryotic CERK. The remaining enzymes in this pathway have little homology to known lipid-modifying enzymes and provide a platform for uncovering new enzymatic activities and mechanisms. Our central hypotheses are that 1) proteins CCNA_01225 and CCNA_01219 are responsible for adding the first glycerate to ceramide 1-phosphate (C1P) to form ceramide-phosphoglycerate (CPG) and 2) proteins CCNA_01210 and CCNA_01217 add the second phosphoglycerate to form ceramide diphosphoglycerate (CPG2). The broader impacts of this project relate to the training of a new generation of STEM researchers for the biotech workforce, at the high school, undergraduate, and graduate levels. Our multidisciplinary team will provide training in bacterial genetics, biochemistry, and structural biology. 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

Regenerative medicine has the potential to heal or replace tissues and organs damaged by age, disease, or trauma, as well as treat disorders occurring across a wide array of organ systems and contexts, including full thickness skin wounds, cardiovascular diseases and traumas, treatments for certain types of cancer, and more. Currently, the transplantation of intact donor organs and tissues faces challenges due to a limited supply and significant immune complications. Bioprinting is an emerging discipline that leverages 3D printing technology to create complex biological structures, including tissues and organs. Among the various innovative bioprinting methods, laser-induced forward transfer (LIFT) is notable for its use of a laser to accurately transfer cells or bioinks onto a substrate, facilitating the development of intricate 3D architectures characterized by high printing precision, improved cell viability, and remarkable technical adaptability. Nevertheless, the characteristics of existing ultra-violet and near-infrared lasers necessitate the use of a dynamic release layer (DRL), which restricts the research and commercial viability of contemporary LIFT technology. We aim to overcome this challenge by developing a novel hybrid laser source that operates near 3 µm, thereby enabling DRL-free LIFT bioprinting technology. With this new laser source, we will print the tissue constructs and assess their viability and integrity after printing. The proposed initiatives, characterized by their international and multi-disciplinary aspects, will create a distinctive learning atmosphere for students involved in the project. Technical description The project involves the design and development of a novel semiconductor master oscillator fiber power amplifier high peak power pulsed light source that operates at the peak of water absorption. Current 3 μm laser sources are limited by either low repetition rates, low pulse energy, or extended pulse widths, resulting in inadequate control over printing parameters when applied to LIFT. To address this, a type-I quantum well cascade diode laser will be developed as the master oscillator to achieve the necessary high power, short pulse, and high repetition rate operation, which is essential for the effective seeding of a specially designed and fabricated multi-stage fiber amplifier. This hybrid mid-infrared laser system will be incorporated into the LIFT bioprinting setup to showcase DRL-free LIFT bioprinting of functional, clinically relevant 3D biotissues. The advanced control capabilities of the new bioprinting system will facilitate the mapping of the donor stage, allowing for the printing of individual cells and, through stage rotation, the creation of a network of fully aligned cells. The use of a galvo scanner will provide a substantial printing area (up to 10x10cm) and enable rapid printing speeds (up to 1M cells/s), thereby enhancing the ability to print cells onto the construct matrix. 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 award funds the research activities of Professors Maria Concepcion Gonzalez-Garcia, Patrick Meade, and Leonardo Rastelli at Stony Brook University. This award supports research in the fundamental laws of physics, and promotes progress in the scientific exploration of the universe. Its research will help guide the measurement and analyze the significance of results from a number of the world's leading laboratories and experiments, and will develop and explore novel concepts for the quantum field theories we use to understand matter and forces at the smallest scales and times as well as matter in its normal and extreme states. It will investigate new possibilities for future facilities in high-energy physics. This research is combined with training at the undergraduate, graduate, and postgraduate levels, and is leading to a broader vision of our world. It crosses disciplines within physics, and explores advanced methods in mathematical and data analysis. It serves the national interest through maintaining the nation's world-leading status in pure science and science education. Among quantum field theories, the Standard Model describes the known forces in nature aside from gravity, including the electromagnetic, weak, and strong forces. Quantum fields are also relevant to physics at all scales, and all phases of matter. Professor Rastelli's research is in the exploration of nonperturbative quantum field theory, pioneering new concepts in the exploration of the properties and the identification of self-consistent sets of possible theories. Current experimental results from high-energy accelerators like the Large Hadron Collider are generally in excellent agreement with predictions of the Standard Model, yet the Standard Model alone cannot explain phenomena like dark matter in cosmology, or the dominance of matter over antimatter. Professor Meade is developing strategies for detecting the limits of the Standard Model, and is a leader in efforts to explore the scientific potential and technical possibility of an unprecedented muon collider. Experiment shows that neutrinos in the Standard Model have the extraordinary ability to oscillate one into another. Professor Gonzalez-Garcia's program of the analysis of ongoing neutrino data is a strong support for progress in particle physics, astrophysics, and cosmology, and she is developing theoretical procedures for using precision measurements at the Large Hadron Collider to identify signs of new forces beyond the Standard Model. 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 research explores projector-based augmented reality (AR) for communication from a robot. Because projected AR has traditionally required relatively flat surfaces to work, this novel approach allows people to view projected images and messages from robots in unstructured environments. In situations like search and rescue, concert halls, and cluttered homes, it can be hard to know what a robot's next actions might be. This approach supports users need to understand the robot's motions and intent when they are moving near people. This research enables robots to communicate by projecting images or messages onto non-flat surfaces. The project uses computer vision approaches to sense the environment and capture the geometry, photometry, and semantics at the same time. It is expected that this will improve communications when the robot is operating near people. This will also allow the robot to provide directions or point to objects without the need for special equipment. The project will do human studies to determine the effectiveness of the communications. This could make robots easier to communicate with in different situations in daily life. The project will directly advance the fields of Human-Robot Interaction, projected-AR, computer vision, and artificial intelligence (AI) by enabling robots to adaptively-project situated visualizations to multiple people at the same time. The proposed solution enables robots to project onto non-flat surfaces with flexible textures. Prior research has focused primarily on flat and mostly smooth surfaces. An adaptive projector-robot system will be created and evaluated to ensure people prefer and accept the system. The project uses unified computer vision techniques to understand an object’s geometry, photometry, and semantics at once. As the robot understands its environment, it can project onto objects that are not next to each other. The robot can dynamically adjust the image, so it compensates for textured surfaces. This is very important for the acceptance and use of robots in different environments. To make sure it works, the researchers will test different objects, scenarios, and locations (e.g., cluttered homes, lecture halls, and search and rescue scenes). The project will recruit participants to see whether they know what the robot is projecting and how to respond to the robot's communications. The project will compare different projection behaviors in real-world group settings. The goal is to measure the preferences and acceptance levels of different groups of people interacting with robots. 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.