SUNY at Stony Brook

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.

NSF Awards · FY 2025 · 2025-08

Electrolytes and electrode–electrolyte interfaces (EEIs) are critical to the performance, safety, and longevity of next-generation electrochemical technologies, including batteries, fuel cells, and electrochemical reactors. However, the complexity of liquid-phase systems and solid–liquid interactions has outpaced current simulation tools, which are largely designed for crystalline solids. The MISPR project (Materials Informatics for Structure–Property Relationships) addresses this gap by building a robust, open-source computational framework that integrates quantum chemical simulations, classical molecular dynamics, and machine learning to accelerate the discovery and understanding of complex electrolytes and electrode–electrolyte interfaces. This project will serve a growing ecosystem of academic researchers, national laboratories, and industry partners seeking scalable and reproducible tools for electrolyte design. To ensure national impact, MISPR will be openly distributed and supported through hands-on training, tutorials, and course modules accessible to all American institutions and communities across different geographic, socioeconomic, and educational backgrounds. The MISPR platform enables multiscale modeling of electrolyte solutions and electrode–electrolyte interfaces by integrating density functional theory (DFT), classical molecular dynamics (CMD), and machine learning (ML) in a unified and modular framework. This award will expand MISPR in four key directions: (1) automation of high-throughput multiscale simulations for electrolyte and EEI systems; (2) development of hybrid DFT-CMD-DFT workflows for spectroscopic predictions (NMR, IR, Raman); (3) integration of optimization algorithms and ML models to prioritize candidate electrolytes based on key performance metrics; and (4) expanded support for open-source simulation engines and force fields to broaden accessibility. All simulation data and workflows will follow FAIR (Findable, Accessible, Interoperable, Reusable) principles and be made available via open databases and APIs. MISPR will be delivered through conda, PyPI, and GitHub with strong emphasis on documentation, reproducibility, and training. Community adoption will be supported through collaborations with experimentalists, computational scientists, and educators, as well as sustained outreach including workshops, user support, and curriculum development for advanced materials education. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemical, Bioengineering, Environmental and Transport Systems in the Engineering Directorate, and the Division of Materials Research in the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Nontechnical abstract: This is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries. Understanding biological responses to environmental variation is a fundamental challenge facing ecologists. To generate accurate predictions of species distribution and persistence it is necessary to understand how mechanisms such as organism interactions and physiological traits shape responses. Seabirds are key consumers in the Southern Ocean, and while changes in their populations have been correlated with environmental modes, the mechanisms underlying these relationships are not well understood. Both ocean and atmosphere conditions are important for seabirds as they forage at sea but breed on land, and changes to wind patterns and Antarctic sea ice location and extent will influence seabird life history. This project focuses on giant petrels (Macronectes spp.), large and dominant avian predators and scavengers that prey significantly on, and influence populations of, species such as penguins and albatrosses. Giant petrels are thought to rely on dynamic soaring for flight, which allows them to use the wind to move while expending little energy. However, quantitative studies demonstrating how giant petrels use wind and the role that wind plays in constraining their distribution are lacking. Also, recent studies suggest that giant petrels may rely on sea ice for foraging, but the impact of sea ice seasonal and temporal dynamics on their population is not clear. Knowledge of the mechanistic links through which sea ice and wind conditions influence giant petrel diet, habitat use, and predation pressure can improve predictive capability for their populations in Southern Ocean ecosystems. Technical Abstract: This is a project jointly funded by the National Science Foundation’s Directorate for Geosciences (NSF/GEO) and the National Environment Research Council (NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award recommendation, each Agency funds the proportion of the budget that supports scientists at institutions in their respective countries. Despite their important ecological roles as predators and scavengers, giant petrels have received far less attention than other well-studied Southern Ocean seabird species such as albatross. This research will improve the current understanding of giant petrel ecology in the Southern Ocean by developing a mechanistic model linking environmental variability in wind and sea ice with foraging energetics. The project also aims to link those environmental drivers with petrel predation pressure on penguins and albatrosses and assess implications for population trends. The project approach will enable connection of individual energetics with landscape-scale environmental variability and will provide new insight into the role of environmental variation in structuring biological processes. Understanding the environmental effects on threatened seabird population foraging may be useful for developing effective management plans. The project will also provide a science communication internship for a graduate student, work with a science journalist to generate feature articles for popular wildlife magazines, and utilize parts of the project dataset in a graduate-level environmental modeling course. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Crocodyliformes, the reptile group including crocodiles, alligators, and their extinct relatives is a key vertebrate lineage that originated approximately 230 million years ago. There are fewer than 30 living crocodyliform species surviving today and all are semi-aquatic ambush predators. However, the extinct relatives of modern alligators and crocodiles were incredibly diverse during the Cretaceous period (~145-66 million years ago) with body sizes, shapes, and habitat preferences rivaling modern mammals. Crocodyliforms were apex predators on land in the southern hemisphere, acquired their modern semi-aquatic ambush ecology multiple times, evolved to have generalist and even plant-eating diets, and even became some of the largest most dominant sea-going predators. This project will focus on understanding this spectacular radiation of reptiles by delving into the its evolutionary roots in the Jurassic Period, 100 million years earlier. The investigators will examine fossil specimens in museum collections around the world, as well as the skeletons of modern species, to assemble a comprehensive, precise history of crocodyliform origins. The researchers will use these data to reconstruct the crocodyliform family tree and redescribe and illustrate key Jurassic specimens based on first hand observation and high-resolution x-ray CT imaging, allowing the researchers to view previously unknown internal features that will help clarify their evolutionary relationships. The outcome of this project will an unprecedented reconstruction the crocodyliform tree of life, illuminating their evolution during the “Crocodyliform Dark Age” of the Jurassic. The researchers will apply cutting-edge biogeographic models and methods incorporating information from continental fragmentation and climate to test how and where these groups moved across the globe over broad time scales. These novel approaches will allow for a comprehensive look at abiotic continent-scale changes and their biotic effects on skeletal adaptation as well as rate and mode of evolution. The project will generate a massive amount of phenotypic and phylogenetic data that will be made freely available to the research community via open access NSF-funded distribution platforms such as MorphoBank and MorphoSource. The project will contribute to the training of the next generation of paleontologists by supporting PhD and postdoctoral researchers. Moreover, the robust morphological phylogeny, constructed using the latest phylogenetic methods, will provide a framework for future work on archosaur evolution. The innovative techniques and widely applicable conclusions from this research will promote interdisciplinary work among paleontologists, ecologists, and evolutionary biologists. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

This award provides travel support for undergraduate student participation in the Symposium on Undergraduate Research. This Symposium will be held at the annual meeting of the American Physical Society Division of Laser Science (DLS), in conjunction with the annual Frontiers in Optics (FiO) meeting of Optica. The conference program includes many of the research topics central to Atomic, Molecular, and Optical Physics. The support of students through this award makes a substantial contribution to the education and training of future scientists. Students who graduate with a background in laser science acquire a broad range of knowledge and skills that enable them to contribute to progress in many areas of science and technology. The meeting is scheduled to be held in Denver, CO in October 2025. It offers an opportunity for undergraduate students to present their research results and to interact with senior scientists primarily from the United States, but also the broader international community. Support is provided only for students who are US citizens or permanent residents. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The State University of New York (Stony Brook University, in collaboration with the University at Buffalo) will procure and operate for at least five years a high-performance, highly energy-efficient, large-memory computer system responsive to the interests and priorities of multiple science and engineering (S&E) and artificial Intelligence (AI)-focused research communities and research applications in national cyberinfrastructure. The project deploys a system utilizing the low-cost and low-energy AmpereOne ARM processors that excel at AI inference, as well as imperfectly-vectorized or pure scalar workloads that characterize much academic research computing but are inefficiently (cost, energy, and performance) supported by current NSF cyberinfrastructure. The large memory and excellent AI inference and generative performance of this processor and Qualcomm AI-200 accelerators will directly advance the mission of the National Artificial Intelligence Research Resource to broaden access to AI by being easily and tightly coupled into diverse applications, yet scalable to tens of thousands of simultaneous jobs or users nationwide. The project employs a comprehensive, multilayered strategy, with regional and national elements to ensure the widest possible benefit of AMA27. The team collaborates with multiple initiatives and NSF projects, reaching an audience that spans high-school students to faculty. Nationally and regionally, AMA27 will support a variety of projects and will provide tailored courses and training (both in-person and remote), host students and faculty, and encourage early registration access and flexible and novel access models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Air traffic control is a vast, distributed operation that requires the efficient and safe coordination of approximately 45,000 commercial airline flights per day in the United States. Safely managing this volume of traffic requires extensive human coordination combined with sophisticated algorithmic planning. Unfortunately, despite multiple overlapping safety layers, serious safety incidents occur at an alarming frequency. The proposed research combines artificial intelligence with formal methods to improve the safety, reliability, and efficiency of air traffic management. Using a combination of voice recognition, predictive models, and safety checks, the project aims to help detect problems before they occur, such as a misunderstanding causing a future runway incursion or a weather-driven delay that cascades across multiple airports. Beyond air traffic control, the developed framework can enhance other cyber-physical systems that combine human coordination with intelligent automation, improving the safety of firefighting operations, strengthening the resilience of the power grid to adverse events, and advancing national security by preventing human error in military operations. This research aims to advance the theory and application of cyber-physical systems (CPS) by improving the safety and resilience of air traffic management. The project investigates a multi-layered approach that integrates formal verification, robust speech understanding, and data-driven disruption analysis. At the airport level, compositional hybrid automata and signal temporal logic (STL) will be used for predictive monitoring of aircraft trajectories and controller-issued commands, enabling early detection of safety violations. At the interface layer, robust machine learning models will extract semantic intent from noisy, domain-specific voice data to support online reasoning and decision-making. At the regional level, scheduling algorithms will be analyzed under operational uncertainty, using formal sensitivity analysis and probabilistic post-mortem inference to identify failure modes and propose mitigation strategies. While focused on air traffic control, the general methods developed are applicable to a wide range of CPS domains that involve human-in-the-loop operation and algorithmic oversight in distributed safety-critical 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 · 2025-08

The award supports research in the field of algebraic geometry, the discipline devoted to the study of polynomial or algebraic equations. Algebraic equations are both beautiful and ubiquitous, as they describe many natural phenomena, from the motion of planets or the shape of leaves and flowers, to the behavior of microscopic particles. The goal of this research project is to study the deeper properties of the solutions to more complicated algebraic equations, called algebraic maps. The investigator plans to continue the long-term investigation of the topology, Hodge theory, and cycle theory of algebraic maps. The close connection between the two main threads of the research, namely the discovery of new and deep aspects of the general theory and the study of fundamental examples, is the motivating principle behind the work. It is anticipated that the results will be of use to mathematicians in algebraic geometry, combinatorics, and representation theory, and to mathematical physicists in the study of string theory. This project also provides research training opportunities for graduate students. The investigator will explore, with various teams of collaborators, the fundamental aspects of the general theory, as well as important examples, through four projects: 1) To determine the exact form of the Decomposition Theorem for the Hitchin morphism for G-Higgs bundles with log-poles over a curve over the field of complex numbers for a reductive group G. 2) To formulate and prove the Topological Mirror Symmetry Conjecture for the intersection cohomology groups of the moduli of G-Higgs bundles for the pair of Langlands dual groups SLn and PGLn with and without poles in the case when rank and degree are not coprime --the coprime case is known--; this requires new ideas, such as generically defined gerbes and a new formalism of correspondences on singular spaces for mixed Hodge modules. 3) To prove a semistable log-pole version of the non-Abelian Hodge theorem in positive characteristic, with cohomological applications. Several new ideas need to be introduced, such as log-connections for affine group schemes and their residue spaces. 4) The PI has already generalized the Alper-Hall-Rydh local structure theorem for algebraic stacks to morphisms of stacks, with geometric applications to the (intersection) cohomology of members of families of good moduli spaces; the PI plans to prove analogous geometric results for the members of families of stacks and to then apply the resulting theory to important families of moduli stacks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Algebraic geometry is the study of geometric objects defined by polynomial equations. It is a richly structured subject at the intersection of several areas of mathematics, combining tools and ideas from algebra, geometry, topology, representation theory, and number theory. This project focuses on one of the core questions in the field: the classification and structure of K-trivial varieties. These include elliptic curves, which underpin modern cryptography, and Calabi-Yau threefolds, which play a central role in string theory as geometric models of the universe. The questions explored in this research are fundamental to current developments in mathematics and connect with related disciplines such as physics and computer science, offering promising avenues for real-world applications. A key component of the project is the involvement of early-career researchers, especially doctoral students, who will contribute directly to the development of new techniques and their application to the problems under investigation. This project will address two foundational questions in the study of K-trivial varieties: the structure of hyper-Kaehler manifolds and the compactification of moduli spaces of strict Calabi-Yau manifolds. First, the classification of hyper-Kaehler manifolds is one of the most elusive aspects of contemporary algebraic geometry; the most recent breakthrough in this direction was made by O'Grady over a quarter century ago. Recent methods and perspectives enable significant progress. A central aspect of the project is the investigation of deep connections between hyper-Kaehler manifolds and representation theory. In particular, the PI aims to classify Kummer-type hyper-Kaehler manifolds and, more broadly, to understand the symplectic correspondences between hyper-Kaehler manifolds and abelian varieties. Second, for strict Calabi-Yau varieties, the absence of compact moduli spaces is a significant challenge. The project seeks to develop a Hodge-theoretic approach to compactifying Calabi-Yau moduli spaces, building on the PI's earlier work with Griffiths, Friedman, and others. The primary tools for addressing these questions are Hodge theory, birational geometry, representation theory, and the recently developed theory of higher Du Bois and higher rational singularities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The recent growth in data-enabled science and engineering has ushered in a promising new generation of cyberinfrastructure (CI) technologies. These technologies use sophisticated features that have propelled the adoption and advancement of artificial intelligence (AI) in scientific and engineering research and discovery. While these technologies hold enormous potential to accelerate scientific discovery, their novelty and complexity often present significant barriers to effective use by researchers. This project, ByteBoost 2.0, offers a community-driven unified training platform where interested researchers and instructors can learn to use state-of-the-art sophisticated technologies. The program offers a series of specialized online training events followed by an in-person hands-on researcher-training workshop. The program enables researchers to gain the skills and strategic understanding required to effectively place scientific workflows on cutting-edge CI resources and train the new generation of researchers, accelerating the process of scientific discovery and innovation. Building on the lessons learned from a successful Pilot program, the ByteBoost 2.0 training platform advances the goals of familiarizing researchers and educators with advanced novel computing technologies. The program is modular and focuses on common challenges faced by researchers rather than a specific accelerator technology or discipline. The program offers an environment that accommodates interested campus, regional, and national computing technology testbed facilities including NSF-funded advanced cyberinfrastructure resources. The program begins with a webinar series open to a broad audience of computational researchers and instructors. These sessions introduce foundational topics, highlight key distinctions among computing technologies, and incorporate hands-on exercises. All training materials will be maintained in a learning management system. Participating researchers will also get access to existing asynchronous CI-courses to establish a baseline level of knowledge and CI skills. After the webinars, participants will apply to attend a five-day, in-person “Bring Your Own Science” workshop. At the workshop, they will work on a capstone project related to their research. The workshop will include educators who will develop curricular programs in collaboration with educational experts. During the workshop, all participants will work on their chosen research problems using the innovative technologies. Trained peer-facilitators and experienced scientists will offer recommendations on how to effectively utilize these systems. The community-driven focus helps integrate the innovative CI technologies into the growing research and instructional fabric. Working with active CI-engagement groups, ByteBoost 2.0 will broaden adoption of computation and AI at two- and four-year institutions, contributing to the goals of national AI research, entrepreneurial, and educational initiatives. The program will be offered on an annual basis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This research project concerns Markov chains, which are used in probability theory to generate random objects, such as random colorings of graphs, bases of vector spaces, and triangulations of a polygon. Markov chains play a pivotal role in theoretical computer science, enabling the development of sampling and approximate counting algorithms important in statistical physics and cryptography. Another important family of examples includes card shuffle sequences, which have intriguing connections to DNA rearrangements in biology. The main targets of study in this project are mixing times, cutoff, and limit profiles. The time it takes a physical system to reach equilibrium is an example of a mixing time. The cutoff phenomenon describes a phase transition during which the distance of a Markov chain from equilibrium very abruptly drops to near zero. The limit profile captures the limiting value of the distance to stationarity as the size of the state space grows to infinity. The research problems under investigation impact a spectrum of disciplines, including computer science, cryptography, physics, and biology, and the project also offers research opportunities for graduate students. The current research program aims at developing techniques for studying the cutoff phenomenon and determining the limit profile for important Markov chains. Mixing phenomena will be explored using tools from representation theory, combinatorics, and probability theory. The program focuses on a range of models, including particle systems, exclusion processes, random walks on groups, and card shuffling. The primary goal of the research is to develop versatile techniques for bounding mixing times and establishing the occurrence of the cutoff phenomenon. A significant part of the proposal concerns exclusion processes. These processes have evolved into some of the most widely studied particle systems across mathematical physics, probability theory, and combinatorics. Their applications span various domains, from modeling traffic congestion to simulating the motion of molecules in gases. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

NON-TECHNICAL DESCRIPTION: Materials that can undergo large changes in shape when an electric field is applied are very important for many essential technologies—from everyday items like cellphones and washing machines to advanced automotive systems. Among these materials, electrostrictors have attracted growing interest in recent years. These materials are unique because they convert electrical energy into mechanical movement, but not the other way around. The project aims to understand the origin of the electrostriction effect in specific ceramic materials, where a few percent of foreign atoms with the same valency replacing the host atoms may be solely responsible for the field-induced volume change—and the resulting large strain. This insight opens a pathway to the rational design of lead-free alternatives to the dominant commercial electrostrictive ceramics. One of the main difficulties is the lack of methods for identifying the key features—called descriptors—of this effect and help predict which ceramic materials will exhibit this behavior. The project aims at developing a new idea for electrostrictive materials, using zirconium-doped cerium oxide. To explore how it works, X-ray absorption spectroscopy is used, a technique that uses very bright X-rays to track changes in the atomic environment around metal ions when conditions such as temperature, pressure, or electric field change. This work contributes to the field by improving understanding of this class of materials and helping predict new ones beyond cerium-based systems. Technologically, this new group of safe, electromechanically active materials could have wide applications. Students will also benefit through deeper learning in advanced topics in materials science, engineering, chemistry, and physics. TECHNICAL DESCRIPTION: The focus of this research project is the development of atomic-level understanding of new types of ceramic materials, those that combine large electrostrictive strain with low dielectric permittivity and high elastic modulus, thereby potentially making these materials attractive for many important applications. The electrostriction effect is the change in mechanical dimensions of a solid as a result of the application by an electric field but not vice versa. The PI and the international collaborator, Prof. Igor Lubomirsky (Weizmann Institute of Science, Israel), propose a new class of ceramic materials based on the Zr-doped ceria that do not have pre-existing elastic dipoles, yet are capable of generating strains up to 1000 ppm under applied field (as large as in the best commercial, lead-based, electrostrictors). To formulate the conditions for the rational search and design of other materials capable of exhibiting this electrostriction effect, the complex relationship between the descriptors of local structure and the dynamics of nearest neighbors surrounding the dopant (Zr) and the host (Ce) ion must be understood. Atomic-level synchrotron characterization performed under temperature, applied stress, or electric field conditions, provides insights into the effects of the separate responses of different components of this system and identifies the bond length mismatch and bond anharmonicity as likely candidates for electrostriction descriptors. This project offers research opportunities and training at advanced national research facilities at the post-graduate, graduate, and undergraduate levels. The project impacts the field of ceramic materials through the development of fundamental understanding and material selection rules for the new class of electrostrictive materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The Algebraic Geometry Northeastern Series (AGNES) is a series of biannual conferences in the field of algebraic geometry. The conference is hosted on a rotating basis by an association of universities in the Northeast region. This award supports six AGNES conferences, which will be held at Dartmouth College on November 8-10, 2024, at Rutgers University in Spring 2025, at the University of Massachusetts, Amherst in Fall 2025, at Stony Brook University in Spring 2026, at Brown University in Fall 2026, and at the University of Pennsylvania in Spring 2027. Each AGNES conference has two goals. First, each conference promotes the dissemination of cutting-edge research in mathematics. The centerpiece of each conference is a series of research lectures by top mathematicians; there are also educational talks for graduate students and events which promote new collaborations or development of peer relationships. Algebraic geometry is a field in the mathematical sciences concerned with solution sets of polynomial equations. It has deep connections to many other areas of pure mathematics, such as topology, arithmetic, number theory, differential geometry, dynamical systems, and homological algebra. At the same time algebraic geometry has found important applications in many subdisciplines of applied mathematics, including cryptography, complexity theory, mathematical biology, and computer vision. The scientific scope of AGNES is greatly enriched by lectures from neighboring mathematical subjects, such as arithmetic geometry, dynamics, complex geometry, and computational geometry. Further information about conference events can be found at the website: http://www.agneshome.org/ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Medical image interpretation is a complex, context-dependent process that requires nuanced reasoning and decision-making, honed through years of clinical experience. Eye gaze patterns, a rich source of implicit expert knowledge, reveal how clinicians systematically identify and interpret critical features in medical images. This project seeks to integrate these expert visual search patterns into machine learning (ML) frameworks, addressing the current gap in how ML models interpret medical images. By leveraging expert eye gaze as a privileged data source, the project aims to enhance diagnostic accuracy, improve model trustworthiness, and provide more interpretable and clinically relevant outputs. For example, understanding how a radiologist systematically examines a chest x-ray can help in designing algorithms that mimic this process, potentially improving the identification of subtle disease indicators that might be missed by less experienced readers or automated systems. By incorporating expert search patterns, ML models can become more accurate and reliable. The anticipated outcomes include improved clinical decision-making, augmented training for novice clinicians, and explainable artificial intelligence (AI) tools capable of advancing medical care and patient outcomes. This project will develop foundational methods to characterize and integrate dynamic gaze patterns into machine learning pipelines. Algorithms will be designed to capture the spatiotemporal nature of clinical experts’ visual search behaviors and their relationship to the spatial complexity of disease patterns in medical images. These gaze-informed models will leverage representations of scan paths to enhance both predictive performance and interpretability. Furthermore, gaze cues will be incorporated into multimodal models, enabling improved spatial and temporal coherence in tasks requiring image-text alignment, such as automated report generation. Evaluation of these methods will focus on challenging applications in radiology and histopathology, including cardiothoracic disease diagnosis, prostate cancer grading, and addressing class imbalance in long-tailed medical datasets. The project’s technical innovations aim to improve the predictive power of state-of-the-art deep learning models while introducing clinician-informed guidance, ensuring clinically trustworthy outputs. By bridging human expertise with computational models, this research addresses critical challenges in medical imaging and lays the groundwork for significant advancements in artificial intelligence-enabled healthcare solutions. The educational component of this project will promote interdisciplinary collaboration, fostering the next generation of researchers and clinicians adept at developing and utilizing AI-driven healthcare solutions. The methods to be developed are broadly applicable and can potentially influence data analysis across other scientific domains where an observer's gaze plays a critical role in reasoning and decision-making tasks. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Complex manifolds are higher-dimensional geometric spaces defined using the complex numbers. This project focuses on two special kinds of such spaces, namely Calabi-Yau manifolds, and Kähler-Ricci solitons. These types of complex manifolds have a wide array of applications throughout physics and mathematics, and for this reason have attracted a great deal of attention in recent years. This project will focus on constructions and classifications of such complex manifolds. The results will deepen our understanding of the geometry of complex manifolds as a whole and more broadly will further advance our knowledge through connections to other subjects ranging from algebraic geometry, topology, and analysis to physics. The project will also be used to help support researchers interested in contributing to these active and competitive fields. More technically, the project aims to further our understanding of the singularity development both of the Kähler-Ricci flow and of collapsing Calabi-Yau manifolds. Characterizing the development of singularities along the Kähler-Ricci flow is one of the fundamental questions in geometric analysis and is expected to reveal deep ties between geometric analysis, algebraic geometry, and topology. On the other hand, the analogous collapsing Calabi-Yau picture has many applications, including to physics. These two programs fit into a common framework, each with a given singularity model (respectively Kähler-Ricci solitons and complete non-compact Calabi-Yau metrics) and a corresponding type of degeneration (respectively the Kähler-Ricci flow and families of collapsing Calabi-Yau manifolds). The project will (1) construct examples of non-compact singularity model geometries, (2) describe the moduli of these spaces and (3) analyze the corresponding degenerations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This award is for the inclusion of the Multi-function Airborne Raman Lidar (MARLi) and Airborne Doppler Lidar (ADL) into the Community Instruments and Facilities (CIF) cohort of available instrumentation. MARLi and ADL, when combined, can provide simultaneous water vapor, temperature, wind, aerosol, and cloud profiles within the layer of the atmosphere closest to the surface. Measurements of the planetary boundary layer (PBL) are key to understanding the processes that drive societally important weather phenomena, like hazardous weather and air quality. The use of MARLi and ADL as a CIF will also lead to public outreach activities and enhanced education and training through the expanded use of these systems. MARLi is a Raman lidar which can provide simultaneous water vapor, temperature, aerosol, and cloud profiles. ADL is a Doppler lidar which can provide high temporally and spatially resolved wind measurements. MARLi and ADL were designed to operate on research aircraft, such as the NSF/NCAR C-130 and the Wyoming King Air but can also be used on the ground as part of the Planetary Boundary Layer Moving Active Profiling System (PBLMAPS). Research activities that can be enabled by MARLi and ADL observations include advancing our understanding of small-scale interactions between clouds and their environment, investigating air-sea and air-land interactions, documenting boundary layer structure over heterogeneous surfaces and under cloudy conditions, examining the mesoscale atmospheric environments and dynamics, and characterizing urban PBL. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Imagine doing some everyday activity, such as walking down a street to meet a friend at a restaurant. As you do this, your eyes are constantly moving—darting from your phone to street signs to the people and cars around you. Yet, despite what should be a jittery visual mess, we perceive a relatively smooth and stable visual world. For decades, scientists have been theorizing about how the brain creates this illusion of visual stability—recent advances in AI suggest an answer that can finally help crack this human perceptual code. The latest AI-powered vision-language models are becoming very good at recognizing visual objects and understanding scenes, even imagining what should be there and filling in missing parts much like the human brain does. This begs the question of whether these models are making sense of the visual world the same as humans. This project puts these new AI models to the test by having them generate plausible visual scenes, in real time, using only the few input samples that correspond to where a person looked while freely viewing the scene. This person is then shown either the real or AI-generated scene and asked if it is the scene that they just viewed. By identifying generated scenes that people cannot tell from real, cognitive scientists learn more about the semantic variability that objects viewed in peripheral vision might take while still seeming plausible (i.e., stable), and computational neuroscientists learn more about the algorithms underlying human visual stability and scene perception. This knowledge may also contribute to the development of smarter AI systems capable of perceiving the world more like humans do, thereby improving performance in time-critical applications such as self-driving cars and enhancing user experiences in augmented reality contexts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

With the support of the Chemical Synthesis and Chemical Catalysis Programs in the Division of Chemistry, Professor Jeffrey Gustafson of San Diego State University is studying new approaches to modifying fine chemicals and pharmaceutical intermediates. Aromatic compounds are common building blocks throughout synthetic and pharmaceutical chemistry, however the chemical reactions of aromatic molecules to give value-added commodities is often hindered because it results in the generation of multiple products, necessitating costly purification sequences. Dr. Gustafson and his team are studying new chemical reactions aimed at controlling the selective modification of both simple and complex aromatic scaffolds, ultimately resulting in more efficient synthetic strategies towards molecules of interest to the academic and pharmaceutical communities. In addition to providing training in organic chemistry for graduate and undergraduate students, this project also impacts the education more broadly in that it proposes to incorporate Virtual Reality experiences to aid students in the 3-dimensional visualization of molecules. Virtual Reality will also be utilized in outreach programs aimed at SDSU’s general population, as well as local high schools and community colleges. Professor Gustafson and his team of collaborators and students design ‘organocatalysts’ that affect the regioselective C-H functionalization of simple and complex aromatics through electrophilic aromatic substitution and related aromatic radical functionalization reactions. These transformations traditionally yield poor regioselectivities, often preventing them from being adopted as viable synthetic strategies, particularly in more complex settings. For the addition of electrophiles into aromatics, the Gustafson group designs bifunctional Lewis basic catalysts that intercept and direct the electrophile, or electrophilic radical to a specific position. The Gustafson group is also exploring similar strategies for the functionalization of aromatics with nucleophiles in the context of nucleophilic aromatic substitution and vicarious nucleophilic substitution. For the regioselective addition of nucleophiles to aromatics, they utilize a broad array of classic organocatalytic strategies including cation directed catalysis and hydrogen-bonding catalysis. The ability to site-selectively modify simple and complex aromatic systems greatly simplifies the syntheses of molecules and opens the door for these chemistries to be used in the context of late-stage functionalization strategies. In order to allow for the design of better and more efficient catalysts, the Gustafson group is also performing mechanistic studies with collaborators in computational chemistry and electrochemistry. The site-selective modification of complex intermediates that this work enables has the potential to transform how the late-stage structural optimization of pharmaceuticals, materials and other functional molecules are carried out. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Electrochemical systems, such as batteries, fuel cells, and supercapacitors, are essential for creating cleaner energy, powering electric vehicles, and storing renewable energy efficiently. Improving these systems is important for making energy storage safer, longer-lasting, and more effective. At the core of these systems are electrolytes, special materials that allow ions to move and enable energy storage and transfer. By designing better electrolytes, it is possible to create improved batteries and energy systems that support sustainable energy solutions. This project focuses on understanding how electrolytes function at the molecular level. Using advanced tools like artificial intelligence, machine learning, and experimental techniques, the research aims to uncover new insights into how ions interact and move within these materials. These discoveries will help in designing better electrolytes, leading to more efficient and reliable energy storage technologies. Beyond advancing science, this project will benefit society by providing open-access tools and knowledge to researchers and educators. It will train the next generation of scientists and engineers in research, and contribute to the development of technologies that support cleaner energy. These efforts align with national priorities to drive innovation, foster sustainability, and expand the science and engineering workforce to meet future challenges in energy and materials science. The proposed research investigates the solvation structure of liquid organic electrolytes in lithium-ion batteries (LIBs) as a model system. The study aims to identify and understand multiple stable ionic species within multicomponent electrolyte solutions and their impact on transport properties. Using a combination of experimental spectroscopy, computational simulations, and machine learning (ML), the project will achieve the following goals: First, it will develop a large-scale database of experimental NMR spectra, capturing solvation environments in LIB electrolytes. Using advanced natural language processing (NLP) and vision-language models (VLM), data from the scientific literature will be extracted to create a high-fidelity resource for training ML models. Where gaps in the literature exist, new NMR data will be generated through collaborations with experimental researchers. Second, the project will elucidate solvation structures and their dynamics by combining molecular dynamics (MD) and density functional theory (DFT) simulations. This approach will overcome traditional limitations by automating predictions of NMR chemical shifts, enabling the identification of multiple stable species in complex solutions. The proposed work will result in the first systematic open-source augmented NMR spectroscopic database for both traditional and non-traditional nuclei, corresponding liquid composition and metadata, and detailed solvation structure obtained using simulations. Finally, the research will develop an ML-based framework to predict NMR chemical shifts for new electrolyte systems, bridging computational and experimental data. The framework will provide rapid and accurate mappings of solvation structures to observed NMR spectra, facilitating electrolyte design for various 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 2024 · 2024-12

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Stony Brook University. Over its five-year duration, this S-STEM Track 2 project will fund scholarships to 30 unique full-time students who are pursuing bachelor's degrees in electrical engineering and computer engineering, areas of growing need in the state. Students will receive four-year scholarships, starting in their first year, toward degree completion. The project will support scholars by providing financial literacy education and financial counseling, as well as mentoring them toward career-readiness through activities such as externships and research experiences. The project will also foster social engagement and a sense of belonging through networking and community-building initiatives. Given Stony Brook University's commitment to social mobility, this approach has the potential to broaden participation in engineering fields, advance understanding of effective support systems for at-risk students, and study the effectiveness of the project's interventions in promoting workforce entry, career development, and financial wellness. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates in electrical engineering and computer engineering. Activities surrounding the scholarships have three aims: (1) supporting students through financial education toward financial readiness post-graduation, (2) designing and implementing programs to support academic success and career readiness, and (3) strengthening a sense of belonging through peer engagement. The project's effectiveness will be assessed through a rigorous research and evaluation plan. These project components will advance knowledge by building on existing models to investigate factors affecting academic, career, and social engagement among low-income undergraduates. The longitudinal research will assess how psychosocial factors impact students' career trajectories and whether exposure to financial wellness, career readiness education, and professional and peer networks promotes greater career efficacy, academic success, and persistence. Results will be disseminated through publications and presentations, contributing to broader educational practices and institutional policies. This project is funded by the NSF Scholarships in Science, Technology, Engineering, and Mathematics (S-STEM) program, which seeks to increase the number of low-income, academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

The world produces more digital data annually that’s growing exponentially. Storing data for decades is challenging and securing it for so long is even more challenging. This project develops secure, efficient, long-term archival storage systems for digital information that endures beyond a single human lifetime. The project uses a long-term security model that is safe against faster computers and even quantum computers and can protect against malicious "insiders" who abuse their access to secretly steal data slowly over years. Lastly, the project explores how to maintain long-term data securely and efficiently, even if individual storage providers cease to exist. This project develops techniques to store archival data securely and reliably for a long-time using information theoretic security and combinatorial security. The project explores techniques to allow data to survive errors ranging from corruptions of a few bytes to large-scale cloud failures. It then explores the trade-off between additional storage requirements, data security and reliability, and system performance. Empirical evaluation of a prototype system provides insights into real-world issues in implementing these techniques over the project’s lifespan, and a simulator embodying these techniques allows projection of the techniques’ effectiveness over much longer time frames. This project fosters collaborations across systems, theory, and security researchers to develop practical techniques to both secure data for many years and ensure that the data endures unchanged. Storage must meet both criteria for a society based on digital data to rely on it. All source code for a prototype system and simulator is maintained and released publicly. Material from this project is integrated into graduate level courses and a new “Secure Storage Systems” course. The project recruits and co-advises several under/graduate students, with a focus on female and Hispanic students, both traditionally underrepresented in computer systems research. The project's artifacts—software, source code, data sets, secure archive simulator, traces, and results—are all embodied in a system called "SecArch: Secure Archives". Results will be disseminated using peer-reviewed publications and arxiv.org. All artifacts will be made public through the project Website: https://www.filesystems.org/secarch. The project plans to maintain the site for at least ten years following the end of the project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Non-Technical Summary This award supports theoretical and computational research and education to enhance the accuracy and efficiency of first-principles quantum mechanical simulations, which are essential for understanding the electronic structure of materials at the atomic level. In today's rapidly evolving technological landscape, developing new materials with superior properties is crucial for advancing modern technologies and industries vital to the US economy, such as electronics, energy, and healthcare. These simulations rely on approximate theories, creating a challenging tradeoff between accuracy and computational cost. Finding a way to make this tradeoff more favorable for accuracy without significantly increasing computational cost is critical. By leveraging advanced machine learning and artificial intelligence techniques, the research team seeks to create innovative methods that refine these approximations, potentially leading to the discovery of novel materials tailored for specific applications. This initiative not only contributes to materials science but also underscores the importance of education and mentorship in fostering the next generation of scientists. The research team is dedicated to preparing students for successful careers in both academia and industry by equipping them with essential skills in artificial intelligence and innovative research practices. By actively engaging with students, the project aims to nurture new talent within the scientific community, paving the way for breakthroughs that can address pressing real-world challenges. Additionally, the new methodologies developed from this project will be incorporated into libraries used by standard electronic structure software packages, which will be made freely available to the research community. Technical Summary This award supports theoretical and computational research and education towards enhancing the accuracy and efficiency of Density Functional Theory (DFT) simulations, a standard method for studying the electronic structure of materials at the atomic scale. While DFT offers a balance between accuracy and computational cost, it relies on approximations that can limit reliability. This project aims to develop innovative approximations to the exact functional using advanced machine learning techniques. The key developments include: 1) Database Optimization: Compiling a comprehensive database of solid materials to inform the development of new approximations. 2) Machine-Learned exchange and correlation models: Implementing new functionals within the established Jacob's ladder approach to ensure compatibility with standard electronic structure codes. 3) New Functional Design: Utilizing non-conventional descriptors to optimize the modeling of strong correlations in solid-state systems. The project addresses the urgent need for improved materials design, with significant implications for industries relying on DFT calculations. By applying machine learning to develop more accurate approximations, the research will contribute to the discovery of new materials. This initiative not only contributes to materials science but also underscores the importance of education and mentorship in fostering the next generation of scientists. The research team is dedicated to preparing students for successful careers in both academia and industry by equipping them with essential skills in artificial intelligence and innovative research practices. By actively engaging with students, the project aims to nurture new talent within the scientific community, paving the way for breakthroughs that can address pressing real-world challenges. Additionally, the new methodologies developed from this project will be incorporated into libraries used by standard electronic structure software packages, which will be made freely available to the research community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Functional diversity refers to the range of biological functions and ecological roles that organisms within a community perform. While functional diversity has been measured in various modern communities, it remains unknown how it changes over long timescales or how it responds to sudden shifts in climate. The Paleocene-Eocene Thermal Maximum (PETM) was a period of rapid, intense global warming ~56 million years ago that is well preserved in the Bighorn Basin, WY. This PETM record documents changes in temperature and aridity, fossil leaves and pollen, and a sample of 20,000+ fossil vertebrates in a precise temporal framework. This project analyzes changes in the functional diversity of mammal communities from before, during, and after the PETM. Elucidating the relationship between mammal functional diversity, floral composition, and abiotic factors such as temperature is key to understanding the biotic response to changes in climate. To achieve these goals, this project develops new, publicly-available AI tools with broad flexibility for paleontologists, recruits undergraduate students for training in STEM research, and implements a fieldwork education program designed to train the next generation of paleontologists. To characterize mammalian functional diversity, this project generates rich 3D datasets from micro-CT scans of mammalian molars, tarsal elements, and distal phalanges. A new set of AI tools will be trained on diverse pilot datasets to rapidly isolate, segment, and crop 3D surfaces of these elements for functional measurements. Measurements include molar dental topography, tarsal facet curvature, and linear measurements of distal phalanges from a stratigraphically-resolved fossil sample spanning the PETM. Measurement of functional diversity permits characterization of the mammalian fauna without resolving many challenging and complex questions about taxonomic affiliations while directly testing whether abiotic drivers such as temperature or biotic drivers such as plant community composition have a greater influence on mammalian functional diversity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Fully Homomorphic Encryption (FHE) allows arithmetic operations on encrypted data without decryption, ensuring that the decrypted result matches the outcome on unencrypted data. FHE has potential to enable privacy-preserving machine learning services in regulated fields like healthcare, finance, and insurance. However, managing ciphertext scales is challenging for programmers. This project proposes new FHE compiler techniques that support automatic scale management to ease programming and optimize latency and accuracy. If successful, these techniques will allow programmers easily and fully exploit recent advances in FHE schemes, significantly improving data privacy during computation offloading. This project includes three research tasks. In Task 1, the team of researchers proposes a new scale management solution to minimize FHE program latency without costly iterative exploration. In Task 2, the team will introduce an automatic bootstrapping management technique to optimize bootstrapping placement and reduce latency. In Task 3, the team plans to develop a new extensible hardware abstraction to handle diverse hardware accelerators and their libraries, improving integration with FHE compilers and accelerators. The results of the project will be available as open source documents and tools. Educational curriculum will be created to educate students and train practitioners. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

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