Cornell University

NSF Awards · FY 2026 · 2026-04

Tropical cyclones are significant natural hazards that cause economic losses and loss of life through extreme winds and flooding, as evidenced most recently by the impacts of Hurricane Helene in 2024. While it is well-known that El Niño, a transient warming of the Pacific Ocean relative to other areas on Earth, typically suppresses Atlantic hurricane activity, it remains unclear if a long-term warming of the Pacific Ocean, relative to other areas on Earth, will have the same effect. This project seeks to bridge that knowledge gap and provide critical insights that will improve long-term hazard preparedness and risk management for impacted coastal communities. The project will also contribute to workforce development through graduate training and ensure broad accessibility of findings through open-source tools and collaboration with risk management stakeholders, advancing national resilience. The primary technical objective of this research is to investigate how Atlantic tropical cyclone (TC) activity responds to reductions in the equatorial Pacific zonal sea-surface-temperature (SST) gradient across different timescales. The project focuses on disentangling the differences between the transient and equilibrium responses of Atlantic TCs to Pacific SST patterns. The research utilizes a hierarchical modeling approach to identify the specific physical mechanisms at play, ranging from single-column models and idealized general circulation models (GCMs) to the high-resolution physics-based downscaling of comprehensive Earth system models. By bridging these theoretical and applied frameworks, the study aims to determine whether the suppression of hurricanes observed during short-term El Niño events holds true for longer-term, forced SST shifts. These activities will provide a rigorous physical framework for understanding large-scale atmospheric controls on hurricane variability, leading to more accurate projections of TC activity in a changing climate. 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

Artificial Intelligence (AI) is becoming increasingly ubiquitous, but with that ubiquity comes a sharp increase in data center energy consumption. This project will develop new algorithms that enable highly efficient training and deployment of AI models, contributing to the United States' strategic advantage in AI capabilities. On the training side, the new algorithms will make smarter decisions about how to explore the vast design space of possible AI models, discovering higher-quality models while spending less time and energy training them. On the deployment side, also called "AI inference," the new algorithms will make smarter decisions about the order to schedule incoming streams of AI tasks, enabling AI systems to run with higher throughput (more tasks done per second) and lower latency (less time waiting for task results). These algorithms' reach will extend beyond AI: the exploration algorithms could help with engineering design problems like drug discovery and fusion reactor design; and the task-scheduling algorithms could help reduce waiting times not just in other computer systems, but also services familiar from everyday life like food delivery and urgent care clinics. The project will approach the two core problems, namely AI training and AI inference, with a classical but under-used theoretical tool: the Gittins index. Roughly speaking, the Gittins index can act as a "universal prioritizer," summarizing all the information one has about a complicated task as a single numerical priority. The research will develop new versions of the Gittins index that make it practical for AI training (specifically, by developing Gittins index acquisition functions for exotic Bayesian optimization problems) and AI inference (specifically, by developing Gittins index schedulers to optimize tail latency in queues). Outcomes of the research will include: (a) new theoretical descriptions of Gittins index algorithms; (b) theoretical proofs that the new algorithms are optimal or near-optimal under certain assumptions; (c) open-source prototype implementations of the new algorithms; and (d) pilot studies demonstrating the effectiveness of the new algorithms. In addition to the new algorithms, the project will develop teaching materials for the discipline of performance modeling, which underlies the task-scheduling side of the project, that break down the advanced math required to understand today's complex computer systems to a wide range of audiences, from a course curriculum for undergraduates to a video tutorial series for working engineers. 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 project studies how people share information and govern their communities in social media platforms designed for private groups. Many people are moving to platforms designed for small groups that offer strong privacy protections, including encryption and closed group memberships. These groups allow people to share news, coordinate activities, and stay connected with friends, families, and communities. However, these platforms often lack tools that help members set shared rules, resolve disagreements, or manage their communities. Instead, current systems usually rely on centralized decisions made by platforms rather than by the communities themselves. As a result, group administrators often receive little support, while members have few opportunities to participate in shaping the rules that guide their discussions. This project will advance methods for ethically studying these groups and develop new ways to help private groups discuss norms, manage disagreements, and govern themselves more effectively. The project team will also develop public-facing educational videos and conduct workshops with U.S. civil society organizations to broaden the impact of the work. This project develops tools and design approaches that help members of private social media groups support constructive discussion and democratic participation. The research plan includes three main activities. The first activity examines how group administrators and members create and enforce rules in private groups using interviews, surveys, and analysis of online discussions. The second activity uses participatory methods to design tools that help group members collaboratively set shared rules and manage their groups. The third activity explores the design of digital tools, including conversational agents, that encourage people to reflect on diverse viewpoints and engage in constructive discussion online. This activity uses technology probes, focus groups, and controlled evaluations to develop and test low-cost prototypes of tools that help group members engage in reflective and deliberative dialogue. Partnerships with community organizations, combined with sustained student-community collaborations on real-world projects, will widen the space of designs that can be explored and increase the chance the ideas from the project propagate into the world. 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.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Maternal antibodies (MatAbs) pose a significant challenge to neonatal immune responses, leading to reduced vaccine efficacy and heightened susceptibility to preventable viral infections. This interference phenomenon is particularly critical in the context of rotavirus (RV) vaccination, which causes a substantial global burden of infant gastroenteritis, particularly in low-middle income countries (LMIC). While various factors have been proposed as contributors to reduced RV vaccine efficacy, the elevated levels of MatAbs in LMIC mothers have been negatively correlated with vaccine efficacy in multiple studies. Currently, the molecular mechanisms responsible for MatAb interference with RV vaccination are unknown. This project aims to bridge this knowledge gap using a valuable human clinical samples and studies in mice. Firstly, this project will take advantage of a new mouse model of MatAb interference, whereby pups vaccinated with live attenuated RV display significantly reduced seroconversion if MatAbs are present. A significant challenge in MatAb research till now has been the absence of a suitable system to study interference at the molecular level. The new mouse model provides a valuable opportunity for a mechanistic evaluation of interference hypotheses, and our preliminary data has identified vaccine clearance as a key mechanism of interference. We now aim to advance our understanding of this using transgenic mouse models and recombinant monoclonal antibody technology to identify the cell types and biophysical antibody features responsible. We will perform comprehensive analysis of RV-specific MatAbs in bio-banked human samples to validate and expand data generated from our mouse model. We will study samples from mothers that have been naturally infected with RV and their infants that have been vaccinated for RV. A panel of RV-specific biophysical and functional antibody assays will be used to determine if there is a subset of RV-MatAbs that are more closely associated with MatAb transfer or vaccine interference. We aim to identify the relative contributions of placental MatAbs and breast milk MatAbs in mediating interference. Successful completion of this project will provide critical insights into which MatAbs are associated with vaccine interference in humans and elucidate the mechanisms by which these are mediating interference. This knowledge will inform the development of new vaccination strategies, potentially identifying promising vaccine approaches that are capable of eliciting protective immune responses in the presence of MatAbs. Ultimately this research will therefore contribute to global efforts to combat RV-induced infant morbidity and mortality.

NIH Research Projects · FY 2026 · 2026-04

SUMMARY Bacterial growth and morphogenesis must be carefully orchestrated in the face of changing environmental conditions to ensure bacterial survival. The primary structure required for cellular integrity maintenance is the bacterial cell wall, mostly composed of the mesh-like peptidoglycan (PG), which surrounds the cell as a covalently-closed macromolecule. PG is synthesized by the concerted action of several PG synthases, namely the Penicillin-binding proteins (PBPs) and associated proteins, which collectively catalyze the formation of the characteristic PG macrostructure, i.e. a polysaccharide backbone crosslinked by short peptide bridges. However, in addition to synthesizing PG, bacteria must also cleave the PG meshwork, presumably to make space for the insertion of new material to promote growth. This activity is catalyzed by so-called “autolysins”, a group of poorly- understood enzymes, including the endopeptidases (EPs) and lytic transglycosylases (LTGs). EPs and LTGs cleave the PG crosslinks and backbone, respectively, and contribute to growth and morphogenesis in a poorly- understood way. This cleavage activity is exploited by some of our most powerful antibiotics, the b-lactams, because a major consequence of their inhibitory effect on PBPs is autolysin-mediated cell wall breakdown, and consequently growth arrest or lytic death. However, autolysin activity is also essential for growth, positioning these enzymes as unique novel targets for the future development antibiotics, since both inhibition and activation of EP/LTG would stymie bacterial growth. We currently have an incomplete understanding of how EPs and LTGs are regulated, depriving us of novel pathways for the development of antibiotics and their adjuvants. Experiments proposed here will fill this gap and we fundamentally aim to understand how autolysins promote bacterial growth and morphogenesis. We will undertake a thorough interrogation of endopeptidase structure, function and physiological role. We ask which regulatory mechanisms ensure properly directed EP cleavage activity, and how EPs interface with the cell wall synthesis apparatus. We will also interrogate the enigmatic LTGs, whose physiological functions are poorly understood. We will leverage our recent genetic screens that have identified novel LTG-dependent functions collectively required for bacterial growth and morphogenesis to define the collective physiological roles of LTG. Overall, our data will inform new conceptual models of cell wall turnover in bacterial growth and morphogenesis, and uncover new potential targets for antibiotics and their adjuvants.

NSF Awards · FY 2026 · 2026-04

This award is to support speakers and participants at The Cornell Topology Festival, an annual conference at Cornell University, to be held May 1-3, 2026. The festival has been a force in the mathematical life of topologists and geometers in the northeastern United States since 1963, providing an arena for the development and dissemination of a broad array of results from within algebraic, differential, and geometric topology and allied subjects. The activities of the festival are designed to encourage mathematical breadth and exchange of ideas between different branches of topology, as well as to welcome young mathematicians into the field and promote interactions between junior and senior participants. Each year, several of the talks cluster around one significant theme, in order to communicate a recent research development in one subarea to topologists across other areas, and to educate graduate students and junior researchers on a subject of recent interest; this year, the theme is "Degenerations and moduli spaces," and there are additional talks ranging from homotopy theory to geometric topology. The festival starts with a day of introductory talks by junior speakers, and a colloquium talk on the history of the topic chosen for the main theme. At the end of the second day there is a lightning session of short talks on exciting new research directions. All these activities serve to showcase developments across interrelated fields at an accessible level. The area of interest for the 2026 festival is degenerations and moduli spaces. About 1/3 of the talks will be on this subject, in order to introduce topologists across sub-disciplines to this area in more detail than obtainable by attending a single talk or mini-course. The remainder of talks at the festival showcase a broad range of recent developments in different areas of topology, selected with a view towards breadth. Additionally, all speakers participate in a “lightning round” consisting of short pitches of recent exciting work by others and speculation on future trends. This gives a forward-looking perspective on new developments and stimulates discussion. The opening introductory day of activities and graduate student talks to provide a point of entry for interested non-experts and to encourage graduate student involvement. The broader impacts of the activities include a more broadly-trained community of topologists, able to transcend the boundaries of subspecialties; a more rapid integration of early career topologists into areas of current research; and the enhancement of collaboration among researchers in different areas of topology. The training effects of the Festival will be extended by dissemination of lecture notes from the workshops and summaries of the panel discussion and problem session; the main vehicle for this is the Festival web site, which has been maintained continuously since 1997. https://e.math.cornell.edu/sites/topology/2026/index_2026.php 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 develop the mathematical tools required to build robust Artificial Intelligence (AI) systems that remain reliable in unpredictable, real-world conditions. While current AI models are highly effective in controlled settings, they can be fragile when faced with unexpected data shifts or adversarial attacks where data is geometrically manipulated to cause errors. The project will advance the state-of-the-arts by shifting from defensive methods that only patch specific vulnerabilities to a more universal approach that provides transferable certificates of robustness. This is achieved by training AI models against a generative adversary, a strategy that uses AI to identify the most realistic and challenging data variations a system might encounter. By preparing for these sophisticated challenges, the project ensures that a model’s safety guarantees remain valid even when it is deployed in a new environment. The intellectual merit of the project includes the establishment of new mathematical links between data geometry and robust learning. The broader impacts of the project include the release of an open-source software library called “skDRO,” the creation of new educational materials for students, and the development of more reliable AI technologies that support the national prosperity and security. The technical goal of this project is to resolve the trade-off between computational tractability and certified robustness in Distributionally Robust Optimization (DRO). A major limitation of current adversarial training is that it often produces models robust only to specific, local perturbations, which lack transferability to broader distribution shifts. This project addresses this by using Optimal Transport (OT) to construct ambiguity sets around smooth data manifolds learned by generative models. The research frames the interaction as a game between a learner and a generative adversary, allowing for the discovery of a Nash Equilibrium that reflects the geometric nature of distributional uncertainty. This approach enables the PI to overcome the curse of dimensionality and establish tight, dimension-free generalization bounds. Furthermore, the project addresses dynamical problems by extending these static concepts to sequential settings. Inspired by the control systems community, the research incorporates Causal OT to ensure that robust policies are time-consistent. The final contribution is a unified set of Robust Bellman Equations that provide a rigorous path for learning policies that are provably resilient to misspecifications in the environment’s transition dynamics. 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

Mark Tuckerman and Norah Hoffmann of New York University and Nandini Ananth of Cornell University are supported by an award from the Chemical Theory, Models and Computational Methods program to create new quantum computing algorithms capable of predicting exact quantum properties of molecules under thermal conditions that exist in typical laboratory settings. The types of calculations needed for such predictions cannot be performed on classical computers, and an important overarching goal of this work is to develop new molecular simulation algorithms for quantum computers and to demonstrate their quantum advantage. The team of investigators will simultaneously incorporate thermal and quantum effects on molecular properties using an alternative formulation of quantum theory established by the physicist, Richard Feynman, based on a “sum over histories” (also known as a “path integral”), which provides a natural, yet largely unexplored, framework for performing quantum computations at finite temperature. The team will target different emerging quantum computing architectures, including spin-based, quantum resonator-based (bosonic), and topological constructions, and they will create quantum computing strategies for studying molecules in their isolated states and under the influence of structured light, which can be exploited to tune specific molecular properties. Due to the rise of quantum science as a national priority, the team will create new educational opportunities, including internships, course materials, and summer programs, designed to train next-generation scientists to work at the interface of physics, chemistry, mathematics, and computer science, in the fascinating world quantum computing. The fields of quantum information science and quantum computing have been identified as national priorities, and within these fields, quantum computational chemistry is one of the target application areas. It is, therefore, critical to anticipate the emergence of these technologies and embark on a program of developing the types of algorithms and computing strategies that will be able to leverage them and uncover and address the challenges in doing so. Among these challenges is the problem of performing quantum dynamical simulations of chemical systems at finite temperature, for which the Feynman path integral formulation of quantum mechanics is particularly well suited. Nevertheless, there have been few, if any, attempts to design quantum algorithms for path integral simulations of full molecular Hamiltonians. In this project, the team of Mark Tuckerman and Norah Hoffmann at New York University, Nandini Ananth at Cornell University, and their groups will address this challenge and develop quantum algorithms for path-integral based quantum dynamical simulations of molecular systems that are tailored to different types of quantum computing architectures. These algorithms will be designed for spin-based, bosonic, and topological quantum computing platforms and will be formulated for property prediction from quantum time correlation functions in isolated molecules as well as molecules in optical cavities subject to electromagnetic fields. Since quantum computing and quantum information science are interdisciplinary, sitting at interface of physics, chemistry, mathematics, and computer science, the team, whose expertise spans these areas, will create new education opportunities at this interface, including internships, course materials, and summer programs, designed to train next-generation scientists in the required interdisciplinary knowledge. 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.

NIH Research Projects · FY 2026 · 2026-03

Project Summary An organism’s phenotype is determined through a combination of genetic and environmental factors. These factors can operate independently, although environmental factors often modulate the expression of genetic factors in a predictable way. The interplay between environmental cues and gene expression can be described as a gene-by-environment (GxE) interaction. Significant effort has been spent working to characterize GxE interactions in human, lab, and agricultural systems because these interactions determine the spectrum of variation in responses to disease, ecological conditions, and other phenomena of broad interest. We therefore have a good understanding of the kinds of physiological processes and gene networks involved in detecting, transducing, and responding to environmental cues in a handful of systems. What we know less about, however, is how GxE mechanisms originate and diversify across species. How does a trait first become genetically responsive to an environmental cue? And, then, how do GxE interactions change over time in different populations or species to adapt to different types of predictable external cues? The goal of our research program is to characterize the molecular basis of variation in GxE interactions within and between species. We use an insect model clade, butterflies, which shows substantial geographic variation in GxE seasonal color pattern phenotypes. Our current focus is on characterizing tissue- and stage-specific gene regulatory networks activated by the steroid hormone 20-hydroxyecdysone, which transduces seasonal environmental cues to target tissues. To complement this work, we use genetic mapping and manipulations to characterize specific genes and regulatory elements involved in regional variation of seasonal phenotypes within and between closely related species. Ultimately, this research will identify developmental genetic mechanisms that allow hormone-mediated GxE interactions to diversify and adapt in nature.

NIH Research Projects · FY 2026 · 2026-03

Convergent molecular evolution, especially among distantly related species, is a hallmark of adaptation, yet the drivers of such convergence (or lack thereof) are typically unknown. Variation in molecular convergence may stem from constraints on evolutionary trajectories, such as how intramolecular epistasis and broader scale interactions among genes differ across lineages. While substantial progress has been made in understanding the prevalence of epistasis for fitness-related phenotypes, particularly in microbial systems, empirical tests of the role of epistasis in convergent molecular evolution are rare, especially in metazoans. A key obstacle is the lack of tractable, highly replicated systems to investigate the extent and generality in the causes of molecular convergence. To meet this need, we have been studying a diverse group of insects which have adapted to cardenolides, a class of steroidal plant toxins that disrupts the biomedically-relevant animal protein, Na/K-ATPase. We recently documented a remarkable 30 independent origins of cardenolide-specialization in insects, spanning 350 million years of evolution (in six taxonomic orders, spanning beetles and flies to grasshoppers). Although a handful of substitutions did indeed convergently evolve in all orders, some species lack these substitutions and others have taken alternative paths. Our findings, which also show distinct patterns among groups (e.g., Coleoptera vs. Lepidoptera, each with multiple origins) suggests lineage-specific constraints of genomic background. This group of insects thus presents a treasure trove of opportunity to decipher the drivers of molecular convergence. How variable are the epistatic interactions between lineages, and do these differences drive alternative outcomes in molecular evolution? Do multiple genes coevolve, shaping patterns of convergence? For example, have ABC transporter genes involved in excretion and storage, which complement resistance to cardenolides, evolved in parallel to Na/K-ATPase substitutions? And finally, do molecular substitutions predictably track the evolution of specific toxins coevolving in host plants? This system allows for some of the strongest general tests of why adaptive phenotypic outcomes have a similar genetic basis. Beyond comparative genomics, which will reveal distinct evolutionary outcomes and genetic associations, we will integrate the power of transcriptomics, in silico models, and functional assays to directly test our hypotheses. Our five-year program is expected to reveal general rules governing when intramolecular epistasis versus broader interactions among genes drive molecular convergence.

NSF Awards · FY 2026 · 2026-02

Warm, rain-bearing clouds play a vital role in the global water and energy balances that help regulate Earth's weather system. Predicting the effects of cumulus clouds (low level clouds with a puffy cotton-like appearance) on weather requires precise quantification of water droplet sizes and their growth rates. Current cloud models are limited by an inability to predict droplet growth rates and measured droplet size distributions (DSDs). This award will combine an accurate numerical model of cloud dynamics with theoretical models for predicting the growth of water droplets into rain drops in tropical maritime clouds. The project will train undergraduate and graduate students in interdisciplinary research. Outreach activities to high school and undergraduate students will be conducted. This award is uniquely positioned to advance cloud physics research on Earth and other planets. Elucidating the physics of droplet growth in the 15–40 micrometer “size-gap” range is essential for accurately predicting the DSDs of droplets and the formation of rain-size drops in warm clouds. Drops grow by processes spanning a wide range of scales from the turbulence-induced fluctuations in cloud thermodynamic properties to the continuum and non-continuum hydrodynamic interactions between droplets. Capturing this physics requires the combination of state-of-the-art large-eddy simulations (LESs) and precise theoretical models for the droplet-scale hydrodynamics including continuum breakdown on close approach. An integrated LES-theory cloud model will be developed to achieve transformative insights into the interactions between large-scale processes such as turbulent fluctuations in supersaturation and temperature and small-scale processes including non-continuum hydrodynamic, van der Waals and electrostatic forces. Varying macroscopic conditions such as the cloud height, temperature and pressure at the cloud base, cloud-boundary fluxes of heat and water vapor, and nuclei concentration while monitoring the resulting changes in turbulent dissipation rate, supersaturation fluctuations, and drop collision rates will reveal the origins of the DSD evolution. 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.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY / ABSTRACT Th17 cells provide critical antimicrobial defense at barrier surfaces, but dysregulated Th17 cell activation leads to chronic tissue damage exemplified by autoimmune diseases including multiple sclerosis (MS). TCR and costimulatory signaling strength are established players in T cell fate specification, and costimulation modulators are being used for autoimmunity and cancer therapeutics. We showed in primary human T cells that CD28 signaling negatively tunes Th17 differentiation through pAkt in a dose-dependent manner. Mouse and human CD28 cytoplasmic tails are highly conserved, but the proline-rich motif is followed by an additional proline in humans that enhances CD28 signaling compared to mice (which have alanine). We generated a novel CD28 ‘humanized’ CD28A210P mouse and show that thymic T cell development is unaffected but pAkt is increased and Th17 differentiation and function are reduced, providing a novel tool to investigate CD28-mediated regulation of Th17 function by enhancing rather than deleting CD28 signaling. We have identified one of the targets of CD28 regulation as the kinase PIP5K1a, generating nuclear PIP2. This led to the discovery that PIP5K1a promotes IL- 17 protein expression through a unique post-transcriptional mechanism involving the mRNA nuclear cap-binding protein ARS2. Excitingly, inhibition of PIP5K1a blocked the IL-17 response to the autoantigen MBP in T cells from people with Multiple Sclerosis. Our new data show that ARS2 deletion in effector Th17 cells prevents disease in the mouse model of multiple sclerosis, EAE, and suggest additional roles for ARS2 in driving pathogenic effector Th17 cells beyond IL-17 production. This proposal will explore these novel mechanisms of post-transcriptional regulation of inflammatory Th17 expression to test the hypothesis that PIP5K1a-ARS2 mediated translation is a critical rheostat regulated by CD28 signaling that determines functional outcomes in Th17 cells in the following aims. Aim 1: How is the PIP5K1a-ARS2 interaction regulated in differentiating Th17 cells? Aim 2: How does PIP5K1a-ARS2 axis regulate Th17 protein expression? Aim 3 Determine therapeutic potential of targeting PIP5K1a and ARS2 in effector Th17 inflammatory responses in vivo. Together, these data will provide much-needed insight into molecular mechanisms that drive inflammatory Th17 cell functions and could be targeted therapeutically. This novel pathway of post-transcriptional regulation of inflammatory Th17 cell protein expression was first identified in human Th17 cells, and has now been reverse-translated into validated mouse models that allow detailed molecular dissection and functional testing in clinically-relevant disease settings.

NIH Research Projects · FY 2026 · 2026-02

Correlational studies in human populations strongly indicate that the environment in which we live has a large impact on our health and well-being, including influencing lifespan and relative rates of aging. Increasingly, it is clear that both the physical and social environments in which we are embedded can shape our health. Establishing causality requires animal experimentation, though experimental manipulation of natural social environments, including social structures or demography is often impossible or challenging. As the model mammal mice have been instrumental in studies of age-related patterns of epigenetic change. There has been a recent push to examine how lab environments may skew or limit understanding of biology, especially of complex processes known to be socio-environmentally sensitive, like age-related methylation. We have established an experimental paradigm that allows us to study mice in replicable, experimentally tractable free- living populations in large outdoor enclosures. By ‘rewilding’ mice we can bridge the gap between ecological and biomedical approaches by using the tools available in biomedicine while studying animals in semi-natural abiotic, biotic, and social environments. We propose to use this paradigm to study how natural complexity in the physical and social environment shapes patterns of aging, as measured by epigenetic changes and behavioral performance. Our preliminary data indicate that rewilded lab mice show faster rates of epigenetic aging in their liver, though whether similar patterns of seemingly accelerated patterns of aging translate to other tissues, behavioral senescence, and lifespan is unknown. We propose to study epigenetic aging in an ongoing population of inbred lab mice that we maintain in a field enclosure. We will compare patterns of genome-wide methylation and behavioral performance in age-matched samples of mice reared in traditional lab environment versus rewilded mice living in an enclosure. In Aim 1, we will assess the impact of living in semi-natural versus lab environments on methylation across multiple tissues. These results will reveal whether differences in epigenetic processes between lab and rewilded mice vary depending on the tissue examined. Using the methylation data, we will generate tissue-specific and multi-tissue clocks in Aim 2 to test how natural environments shape the pace of aging relative to the lab. In addition to methylation, we will also document age- related changes in behavior to document overall patterns of senescence in performance. Deciphering the processes that contribute to the broad-scale shifts in epigenetic regulation across lab and field environments is critical for relating studies of lab mouse models to the biology of free-living mammals, including humans. Once we have firmly established the patterns of age-related changes in methylation and performance in free-living mice through this R21, we will manipulate mouse populations and genetics to experimentally test ecological, social, and genetic factors shaping epigenetic changes in a free-living mammalian population in subsequent grants.

NSF Awards · FY 2026 · 2026-01

This award supports the installation and testing of a 20 Tesla superconducting magnet at the Cornell High Energy Synchrotron Source (CHESS). Since 2021, CHESS and the National High Magnetic Field Laboratory (NHMFL) have worked together to create a new facility to study the properties of materials in high magnetic fields using x-rays – the High Magnetic Field (HMF) beamline at CHESS. The construction of both the x-ray beamline (at CHESS) and the unique magnet (by commercial vendors, jointly overseen by CHESS and NHMFL) were supported by previous NSF awards. In 2026, the magnet will be installed at the beamline and tested. Once this phase is complete, the magnet and beamline will be ready for operations as a national user facility. One of the main science drivers for the HMF facility is quantum materials science. Installation and Site Acceptance Testing (SAT) of the 20 Tesla superconducting magnet for the HMF beamline require effort from CHESS scientists and technical staff including cryogenics, instrumentation, mechanical, machining, and electrical experts. CHESS teams will work with vendor engineers to perform the installation and testing. Successful installation and testing will satisfy several key requirements: (1) The magnet must be installed in a custom-built vacuum enclosure which is free from ferrous material. (2) The magnet must be energized and quench-tested. (3) The magnet must meet design criteria of slow rotation at field and achieve low sample temperatures and ultra-low vibrations. (4) The magnet must be integrated into the existing beamline control and safety systems for future operations. 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

The U.S. pellet market is worth almost $9 billion. The majority of pellets are made from hardwood processing waste, and their applications are limited to heating and energy generation. Biomass-based pellets could replace coal in some industrial settings if they can achieve a higher energy density and strength than wood-based pellets. Converting low-cost, regionally available materials such as food processing waste and fast-growing crops into high-value solid fuels could enhance rural energy independence. However, designing biobased combustible fuels as drop-in substitutes for coal requires advances in fundamental energy science. This project will use experiments and process design to overcome limitations with single char-based solid fuels and design drop-in fuel mixtures that optimize properties between various biomasses. The project will bring together stakeholders from academia, industry, and government to explore implications of bioenergy. Project outcomes will help the U.S. become a leader in biobased fuels. Hydrothermal Carbonization (HTC) is an efficient way to produce solid biofuels from wet wastes. Yet hydrochars (HCs) do not combust like the coal they are thought to replace, owing to the presence of secondary char (SC), a tarry material that forms during HTC. This project will design true drop-in solid biofuels by aligning carbonization levels between hydrochars and torrefied biochars (BCs) to reduce fuel segregation and increase thermal stability and energy density of HC-only fuels. The SC present on HCs may serve as a pellet binder, improving hydrophobicity and tensile strength, but it will alter ignition and burning characteristics due to its higher reactivity and lower porosity. By integrating fuel and combustion science, the project team will develop rapid thermogravimetric analysis-based assessment method to predict key combustion behaviors (ignition delay, ignition mode, combustion mode, burning rate) and how SC impacts these behaviors. Through the development of a statistical model the team will determine the optimal SC composition and HC-BC blend ratios. The project will promote economic diversification in rural areas of the US by identifying key characteristics of feedstocks that optimize biomass-to-fuel conversion and utilization. Three key audiences are targeted in research translation activities to support a Resilient Rural Economy: students in the laboratory and in engineering Capstone Design courses; researchers across the fuel and combustion communities; regional stakeholders from industry, academia, and government. 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.

NIH Research Projects · FY 2025 · 2026-01

PROJECT SUMMARY Circadian clocks confer organismal fitness by anticipating environmental changes brought on by the day/night cycle. At the molecular level, the clock is an autoregulatory transcription-translation feedback loop (TTFL) composed of protein assemblies whose components contain a substantial amount of conformational disorder. While the basic molecular mechanism of the TTFL is understood, the key molecular interactions that underlie the timing of the clock, particularly how disordered regions contribute to clock protein interactions, are unknown. Aberrant clock behaviors are extensive and associated with a wide variety of diseases including neurological, metabolic and immune disorders. As intrinsically disordered proteins are implicated in liquid-liquid phase separation (LLPS), we and others have proposed that phase separation plays a role in the TTFL oscillator. Formation and dissolution of clock protein coacervates may contribute to their spatiotemporal organization and regulate their activities to control clock timing. The filamentous fungus Neurospora crassa contains a core clock mechanism analogous to that of higher organisms and has hence long served as a tractable model system. Here, I focus on the positive arm of the N. crassa clock that is comprised of two proteins, WC1 and WC2, that form the heteromeric White-Collar Complex (WCC). We will investigate the forces that underlie WC1 and WC2 phase separation and, in turn, determine how phase separation influences the structural dynamics and functions of these proteins, including timing and light entrainment. Aim 1 will explore the sequence features, solution conditions, and post-translational modifications that control WC1 and WC2 LLPS and how LLPS, in turn, regulates interactions and blue-light sensing. I will induce LLPS of full-length, truncated and phospho-mimetic constructs under various solution conditions and assess how interactions between the WCC and other clock components change as a function of LLPS. I will also study the effect of LLPS on photoactivation of WC1. Aim 2 will define the molecular interactions between White-Collar proteins and other clock components by determining WC1 and WC2 stoichiometry and dynamics and mapping out interaction interfaces and affinities using diffraction, light scattering, cross-linking mass spectrometry and optical analytical techniques. Additionally, we will obtain structural details of the ordered regions with the WCC using cryo-electron microscopy. These results will provide insight into how intrinsic disorder drives WC1 and WC2 LLPS and how a phase separated compartment influences biomolecular interactions, enzymatic activities and light-sensing capabilities. My work will inform on how aberrations that disrupt condensate formation may lead to clock dysfunction and disease. This project, carried out over three years at Cornell University and in collaboration with clock biologists, will advance my technical skills in molecular biology and biophysical chemistry and grow my expertise as a professional scientist who can lead complex projects in either academic or industrial contexts.

NSF Awards · FY 2026 · 2026-01

This project investigates security and privacy of embeddings, a fundamental building block of artificial intelligence (AI) systems. Embeddings convert text, images, audio, and video into mathematical vector representations whose geometric relationships reflect the meaning of the inputs. Embeddings are a key component in modern search and information retrieval systems, large language models (LLMs), and other generative AI systems. An entire new industry of vector databases has emerged to provide solutions for large-scale storage and management of embeddings. The project's novelties are to tackle, for the first time, security and privacy vulnerabilities that are unique to embedding-based systems and to develop robust mitigations. This includes defending against attacks that exploit embeddings to manipulate LLMs, as well as attacks that extract confidential and private information from vector databases. The project's broader significance and importance include protecting emerging AI systems from adversaries, mentoring students at an early stage of their career, and technology transfer from academia to industry via open-source code releases. The technical approach of the project focuses on three key security and privacy problems related to embeddings. First, it studies inference-time attacks and defenses. This includes robustness to adversarial inputs, especially in real-world systems that operate on multi-modal inputs (e.g., text and images). Second, it investigates training-time attacks that aim to change semantic relationships encoded in the embeddings. Third, it develops new methods for measuring information leakage from embeddings in a variety of realistic scenarios. In summary, this project addresses a new research area of trustworthy AI and helps ensure that AI-based systems can be safely deployed in our digital society. 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

A mathematical knot is obtained by taking a piece of rope, tangling it in some way, and then joining the ends. A classical question in knot theory asks whether two knots can be obtained from each other by continuously transforming the rope. One way to distinguish two knots is to compute their knot invariants. Some of the most powerful knot invariants include the HOMFLY polynomial and its recent generalization known as Khovanov–Rozansky homology. For instance, the HOMFLY polynomial is used in molecular biology to study how DNA molecules are folded in space. In this project, we relate these knot invariants to objects arising naturally in algebraic combinatorics, a field which applies algebraic methods to study discrete objects such as binomial coefficients or triangulations of a polygon. The number of possible triangulations of a polygon is counted by the famous Catalan number sequence. One of the main results of the project gives a natural geometric interpretation of Catalan numbers, by means of relating them to Khovanov–Rozansky knot homology and the HOMFLY polynomial. The objects that appear along the way are interpreted from the point of view of statistical mechanics, which deals with macroscopic observations of a physical system consisting of a large number of particles. For example, the geometric spaces in question are directly linked to the Ising model at critical temperature, which describes ferromagnetic properties of a flat metal plate at the Curie point. The award also provides funding for the involvement of undergraduate students, graduate students and postdocs in the PI's research. The Grassmannian is stratified by spaces known as positroid varieties. In a joint project with Thomas Lam, the Principal Investigator (PI) studies the mixed Hodge structure on the cohomology of positroid varieties. The main result states that the bigraded Poincaré polynomial of the top-dimensional positroid variety is given by the (rational) q,t-Catalan number, introduced in the works of Garsia–Haiman and Loehr–Warrington. The proof proceeds by associating a link to each positroid variety, and relating its cohomology to the Khovanov–Rozansky homology of the associated link. The point count of the positroid variety is therefore given by a coefficient of the HOMFLY polynomial of the link. The PI has recently shown that the point count is given by certain observables in the stochastic six-vertex model. Separately, positroid varieties were connected to the Ising model in the joint work of the PI with Pavlo Pylyavskyy. In this project, the PI uses this relation to give a direct formula for boundary correlations of Baxter's critical Z-invariant Ising model. This formula is applied to questions of universality and conformal invariance of the model, studied by Smirnov et al. 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

Polymers are widely used in modern technologies, including in electronic devices for communications, computing, and defense, where a premium is placed on small size and low weight. However, their poor ability to conduct heat limits their use in thermal management applications for critical industries. This project explores a new class of polymers, ladder polymers, that may offer a breakthrough in polymer-based thermal materials. Ladder polymers have a unique double-stranded backbone structure, like a ladder, which limits the flexibility of their molecules from rotational motion and strengthens chemical bonds between atoms. These features were thought to improve heat conduction of the ladder polymers. However, preliminary findings reveal unexpected behavior: these polymers conduct heat less effectively along a single molecular chain than polyethylene, but they perform better than expected in bulk form. This surprising result suggests that current understanding of how polymer structure affects thermal conductivity is incomplete. By uncovering the principles behind heat transport in ladder polymers, this research not only will help improve future polymer design, but also will support the development of high-performance thermal interface materials for thermal management of electronics. Educational activities, including curriculum development and student research opportunities, will further extend the impact of this project. This research will investigate the fundamental mechanisms of heat transport in ladder polymers using molecular dynamics simulations and vibrational mode analysis. The study will compare heat conduction in individual chains and bulk forms of ladder polymers to that in polyethylene, a well-understood single-stranded polymer. The work will address two scientific questions: (i) how the ladder structure influences heat transport along polymer chains, and (ii) how the rigidity of the backbone affects bulk thermal conductivity. This study will advance fundamental knowledge of nanoscale heat transport in polymers by addressing unexplored aspects of ladder polymer systems. Importantly, this research will expand the existing knowledge base of polymer thermal transport by adding the new structural category of ladder polymers to the broader dataset of polymer heat conduction studies. Insights from this work will guide the rational design of thermally efficient polymers and contribute broadly to materials science, thermal physics, and computational modeling. The project will also support graduate and undergraduate education in polymer science and thermal transport, promoting the training of future scientists and engineers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-11

Low-Dimensional Chiral Perovskites for Circularly Polarized Light Detection Circularly polarized light (CPL) photodetectors emerge as an indispensable component in quantum computing, spin-optical communication, information processing, and remote sensing because of their capabilities of distinguishing the handedness of CPL that encodes charge carrier spin information. The state-of-the-art technologies for CPL detection are based on conventional photodetectors coupled with optical components of linear polarizer and quarter-wave plates to convert CPL to linearly polarized light. These device structures cause substantial losses of sensitivity and handedness selectivity, slows detection speed, and faces a tremendous challenge for device miniaturization and integration. Low-dimensional chiral perovskites, which are composed of corner-shared or face-shared inorganic metal halide octahedra and chiral organic cations, have emerged as promising candidates for direct CPL detection because of their tunable band gaps as well as their spin-selective high absorption coefficients and favorable charge-carrier mobilities. Despite recent progress in developing CPL photodetectors, the device performance still lags conventional photodetectors in terms of specific detectivity, dynamic response, and most importantly, the capability to distinguish the handedness of CPL. Here, the research team proposes a synergetic approach via materials innovation and device engineering to enhance chirality and charge transport/extraction, reduce dark current, and increase dynamic photoresponse, hence, to achieve highly selective, sensitive, and fast CPL detection. The success of this project will advance the fundamental knowledge of chiral semiconductors and CPL photodetectors and could lead to transformative technologies in quantum computing, spin-optical communication, and information processing. Graduate and undergraduate students will receive training in this highly interdisciplinary research project. The knowledge gained from this work will be disseminated through the outreach to local high schools and on-campus workshops for young students with hands-on experience. The objectives of this work are to conduct fundamental research through a synergetic approach from materials development to device engineering to enhance chirality and charge transport of chiral two-dimensional (2D) and one-dimensional (1D) perovskites, leading to highly selective, sensitive, and fast CPL detection. At the material level, chiral 2D and 1D perovskite thin films with tailored n-type semiconducting chiral cations will be synthesized and fabricated to allow increasing the chirality, tuning band structure and energy alignment, and enhancing charge transport. At the device level, CPL detectors will be developed based on the photodiode-type structure with hole-only, electron-only and conventional configurations and the configuration of devices will be further optimized using finite-difference time-domain (FDTD) electromagnetic simulations, transfer matrix method (TMM) optical calculations, and CHARGE solver charge transfer simulations to achieve strong selective handedness of CPL absorption in the chiral active layer and high charge generation rate, charge transfer and photocurrent density with monochromatic CPL irradiations. The designed CPL photodetectors will be fabricated, and their performance will be assessed in terms of the figures of merit of photodetectors. The device performance results will be used to guide the improvement of materials and device structures. The research team will conduct systematic material property, device photophysics and device performance studies to elucidate the material-device structure-device performance relationship of CPL photodetectors based on chiral 2D and 1D perovskites and photodiode-type structures. 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

Many organisms are threatened by exposure to extreme weather – including cold spells. Exposure to cold classically slows or arrests development; however, cold exposure early in life can also change how organisms develop in ways that affect their ability to cope with extreme weather later in life. This project will test whether developmental cold exposure influences the subsequent response to dynamic temperatures, and how this process occurs. It will integrate experimental approaches in wild birds, data from a long-term study population, and analyses of publicly available continent-scale data, to address how dynamic environments affect individuals and populations, and which processes drive resilience and robustness to stress. The broader impacts of this project will also address the vulnerability of the millions of birds that nest annually in nest boxes to extreme weather. Additional experimental studies and analyses of continent-wide data will inform the development of site-specific recommendations for nest box design, which will be disseminated widely in partnership with the Cornell Lab of Ornithology’s Project Nestwatch. This project will address central questions in organismal biology, including the mechanistic underpinnings of organism- and population-level responses to climate change, and how interactions between organisms and their environments determine the emergence of complex traits. Coordinated experiments will assess potential mediators of developmental plasticity, including neuroendocrine systems and transcriptomic and epigenetic processes. A key advance will be in illuminating when ecologically relevant cold challenges trigger adaptive plasticity. Most of what is known about the effects of developmental cold exposure in homeotherms comes from studies in which individuals were reared in captivity, often at consistently cold temperatures. Manipulating the temperature of natural nests is a powerful approach for testing the likely impacts of changing thermal regimes, as the effects of ambient temperatures on offspring can be augmented or buffered by parental behavior. This research will also provide insight into the drivers of resilience (returning to a stable state following disturbance). To understand the emergence of this phenomenon at higher levels, it is necessary to connect sub-organismal processes with their individual- and population-level consequences. Using a combination of experimental, environmental, and large-scale population monitoring and abundance data, this research will test whether developmental plasticity modifies resilience in individuals and in populations. By integrating data across levels this project will also elucidate whether cold-induced plasticity will buffer the effects of dynamic temperatures on a widespread but declining songbird. 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 need for materials with improved or new properties is often at the heart of the challenges society faces. Despite massive expansion in experimental capabilities and data, knowledge- and data-centric challenges prevent prediction-driven materials discovery. The NSF AI Materials Institute (AI-MI) aims to propel foundational AI research past the limitations of existing AI algorithms by pursuing materials discovery and conquering knowledge- and data-centric challenges. AI-MI will advance the foundations of artificial intelligence while accelerating the discovery of next-generation materials essential for sustainable energy, electronics, environmental stewardship, and quantum technologies. AI-MI brings together computer scientists, materials researchers, and data scientists to leverage advances in AI technology and materials data to drive use-inspired progress in fundamental AI, catalyzing prediction-driven materials discoveries. By tightly integrating data generation, AI inference, and rapid experimental feedback, AI-MI aims to reduce discovery cycles from months to days and to establish reproducible, reusable workflows for the broader community. AI-MI plans to create the AI Materials Science Ecosystem (AIMS-EC)- an open, cloud-based portal. AIMS-EC will couple a science-ready large-language model with multimodal data streams (experimental measurements, simulations, images, and textual literature). This platform will allow researchers to pose natural-language queries and receive transparent, data-grounded answers, thereby unifying prediction, explanation, and experimental design in a single interface. AI-MI will apply the capabilities of AIMS-EC to discover two-dimensional moire structures with properties suitable for robust qubits, learn descriptors that can guide the design of new superconductors, discover new functional soft materials and mixtures for sustainability, and identify functional peptides for the removal of microplastics. Moreover, AI-MI will accelerate material synthesis through data-driven optimization of film growth and self-driving labs. AI-MI will implement a comprehensive educational program that covers AI and materials science across all levels of instruction. Through educational modules, internship programs, and partnerships, AI-MI will develop an agile talent pipeline to propel advancements at the intersection of AI and materials science for the next generation workforce, ultimately driving advancements nationwide. Moreover, AI-MI will engage with non-academic collaborators, especially in the materials industry, through training programs and internship placements, in addition to research collaborations on problems of practical importance. 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

Touch and hearing convey physical properties about the world that are difficult to perceive from vision alone. The objective of this project is to give machine perception systems the ability to form cross-modal associations between these three sensory modalities, such as the ability to predict how an object will feel or sound from sight. These cross-modal associations can also be obtained directly via sensors, making them well-suited to creating autonomous systems that learn to physically interact with the world without human-provided supervision. The project's integrated education and outreach activities will also advance an understanding of multimodal machine learning for a general audience, and for students at multiple levels. This project aims to learn material properties and microgeometry through cross-modal associations between sight, sound, and touch. It does this through four research thrusts. First, it aims to capture 3D multimodal representations by registering observations from all modalities into a unified 3D model, using estimated visual geometry to obtain dense estimates of touch and sound from sparse observations. Second, it aims to generate space-time reconstructions of objects from touch and sound during physical interaction, using cross-modal visual supervision. Third, it aims to learn material representations that capture acoustic properties, as well as methods that integrate these representations into 3D sound synthesis models. Finally, it aims to simulate and learn physical interactions within captured 3D multimodal scenes. 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

NON-TECHNICAL SUMMARY Impact-induced erosion from high-speed particle collisions occur at the microscale in critical systems such as jet engine turbine blades, satellites, and chemical pipelines. Ultrafine-grained and nanocrystalline metals, with their superior mechanical properties hold promise for improved erosion resistance, but the underlying mechanisms remain poorly understood. This award is addressing this gap through in-situ studies of supersonic impact-induced erosion in metals across a broad range of grain sizes, from tens of microns down to the nanometer scale. Using in-situ supersonic impact testing, the project examines material response at small scales and extreme strain rates, focusing on understanding the key hardening and softening mechanisms. This research is enabling the design of new materials that can prevent erosive failures, with the potential to strengthen the national defense, automotive, aerospace, and energy industries. This activity is also engaging K–12 students to strengthen the STEM pipeline and enhance national competitiveness. This project supports education and workforce development by introducing new curricula, raising student awareness of emerging opportunities in metals, and connecting industry with the latest research advances. TECHNICAL SUMMARY Deformation localization under supersonic impact is a precursor to erosive failure and is governed by the competition between hardening and softening mechanisms at extreme strain rates. The overall goal of this project is to systematically investigate these mechanisms in ultrafine-grained and nanocrystalline metals, within a strain-rate regime that is largely inaccessible to conventional mechanical testing and as as result, remains significantly underexplored. This research further refines a novel combination of laser-induced microprojectile impact testing and spherical nanoindentation to isolate and quantify dislocation–phonon interactions which are hypothesized to be the primary hardening mechanism under erosive impact conditions. This integrated approach is being used to study how grain size influences ballistic dislocation transport and its role in impact-induced hardening. In parallel, the study is also exploring the microstructural origins of softening, focusing on two key mechanisms, adiabatic shear instability and grain coarsening. Moreover, this project is examining how these are affected by grain size. High-resolution cross-sectional microscopy is being used to characterize microstructural evolution while providing mechanistic insight. Together, this investigation is revealing how the interplay between hardening and softening mechanisms govern a material’s resistance to erosive failure and offer design guidelines to help prevent such failures. This project also supports education and workforce development by introducing new curricula, raising student awareness of emerging opportunities in metals, and connecting industry with the latest research advances. 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

Fruit and vegetable crops are essential to a healthy diet, supplying nutrients often missing from staple grains like corn and wheat. Their high market value also offers important economic opportunities for farmers. Developing new varieties with improved disease resistance, yield, and quality benefits both producers and consumers. However, characteristics that set these crops apart, such as their perishability, also make them harder to breed. A major challenge is collecting reliable data on yield and quality, a process that is often slow, costly, and labor-intensive. This project addresses that challenge by creating faster, more precise ways to evaluate and select for these traits in three of the world’s most important horticultural crops: tomato, onion, and strawberry. It brings together researchers from the U.S., India, Japan, and Australia to apply advanced tools in imaging, machine learning, and genomics to support the development of productive, high-quality varieties that meet the needs of both growers and consumers. This project develops methods for the high-throughput, non-destructive evaluation of yield and quality using both RGB and spectral imaging. To accomplish this, data are collected from both handheld and autonomous devices and fed into deep learning-based image segmentation models to measure traits such as fruit count, size, and shape in tomato and strawberry as well as bulb shape in onion. In addition, the project investigates the ability of models incorporating high-dimensional biological data, including hyperspectral, genomic, and transcriptomic features, to predict complex traits in these crops. The research team also combines 3D modeling and gene expression data to understand and forecast growth in strawberry plants. In parallel, the project fosters international collaboration and capacity-building through research exchanges, workshops, and training opportunities focused on the use of modern phenotyping and predictive tools in plant breeding. 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.