Cornell University

NSF Awards · FY 2026 · 2026-09

This award supports participants of conference "Horizons in Descriptive Set Theory" that will take place at Cornell University from October 10-12, 2026. Descriptive set theory is a branch of mathematics which develops tools for understanding and manipulating infinite sets, particularly in settings where other mathematical structure, such as algebra or geometry, is either not present or minimized. These methods have nevertheless proved very fruitful in yielding new insight into other areas of mathematics such as algebra, geometry, dynamics, and probability. The conference will provide opportunities for researchers to network, for fields outside of set theory to learn of new developments in descriptive set theory, and for set theorists to explore new opportunities for applications for their methods, as well to learn techniques from other fields of mathematics. Descriptive Set Theory has long been at the forefront of applications of set theory to other fields of mathematics. This has been especially true of Ergodic Theory, Topological Dynamics, and Combinatorics. Historically this interaction has been fruitful in both directions, with each field learning techniques from the other and with the applications of descriptive set theory to other fields shaping its own future directions. The conference Horizons in Descriptive Set Theory will showcase the state of the art in applications of descriptive set theory with an eye for future directions in the field. It with bring together senior leaders and rising stars working in descriptive set theory and in fields with which it interacts. The conference will feature 16 invited speakers--7 of which have given invited talks at the International Congress of Mathematicians. The speakers include both people working primarily within Descriptive Set Theory, as well as those working in Ergodic Theory, Topological Dynamics and Combinatorics. Topics covered include classical ergodic theory, orbit equivalence relations of group actions, the dynamics of automorphism groups of countable structures and other transformation groups, structural Ramsey theory, and constraint satisfaction problems. Some of these connections are emerging, while others are well studied but remain fertile. More information about the meeting can be found on the conference website https://math.cornell.edu/horizons-descriptive-set-theory . This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-09

This project develops new mathematical and computational methods for solving inverse problems that arise when scientists and engineers infer hidden causes from indirect, incomplete, or noisy observations. Such problems are central to many areas of national importance, including geoscience, medical imaging, and cancer treatment planning. The project advances the national interest by creating tools that can support faster, more accessible radiation therapy planning, improve the reliability of scientific computing with limited data, and strengthen the foundations of data-driven discovery. By improving methods for extracting useful information from uncertain observations, the project promotes the progress of science and supports advances in national health, prosperity, and welfare. The work also supports Presidential priorities in AI by developing learning-based methods that are reliable, interpretable, and grounded in mathematics. Education and outreach activities broaden participation in science, technology, engineering, and mathematics by providing student mentoring, curriculum development, public engagement, and data science workshops for incarcerated learners and reentry participants. This project establishes a transport-based, measure-theoretic framework for inverse problems by formulating unknown quantities, data, and solution methods over spaces of probability measures. The investigator studies three connected research directions. The first develops dynamical system models and inverse methods for settings in which the governing dynamics are unstable, the inverse problem is ill-posed, data are sparse, and observations are noisy or indirect. The second develops approaches for stochastic inverse problems in which the unknown parameters are inherently random, with an emphasis on uncertainty quantification beyond standard Bayesian formulations. The third develops inverse operator learning methods that use modern machine learning architectures to construct fast solvers for repeated inverse problem instances. The project combines optimal transport, variational analysis, functional analysis on spaces of probability measures, statistical learning, measure-valued flows, sampling methods, and computational algorithms. Expected contributions include new mathematical theory for nonlinear operators on spaces of probability measures, robust algorithms for inverse problems with limited data, scalable methods for uncertainty quantification, and efficient computational tools for applications in geoscience and medicine. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

In this CAREER project, Professor Yao Yang of Cornell University is studying one of the grand challenges of chemical catalysis by capturing real-time nanoscale “movies” of catalytic processes, i.e., watching catalysis in action. The project will use operando methods to transform mechanistic understanding of catalyst-molecule interactions and fill the fundamental knowledge gap in electrocatalysis across multiple length scales. It will advance understanding of the delicate molecule-catalyst interplay in which catalysts are designed to effectively transform molecules into desirable products, while these same molecules often drive (unwanted) catalyst evolution. Meanwhile, the project will make education in electrochemistry and energy materials more engaging and intuitive. Electrochemistry and catalysis are crucial to meeting advanced manufacturing need in an efficient way. However, they are challenging topics for the general public, because electron flow is invisible to the naked eye. The project will design hands-on experiments, such as tomato batteries, and promote electrochemical education at all levels, including K-12, local and national outreach communities as well as undergraduate and graduate students. With the support of the Chemical Catalysis in the Chemistry Section, Professor Yao Yang of Cornell University is studying molecule-driven dynamic catalyst structural evolution from pristine to active structures under operating conditions. This study will investigate the dynamic evolution from pristine homogeneous atoms/molecules into active heterogeneous nanostructures using Cu-based catalysts as a prototypical system for catalytic reactions performed under strongly reducing or oxidizing electrochemical potentials. Multimodal operando electron microscopy and correlative X-ray methods will be used to identify key catalytic activity descriptors of nanostructures including their structures, valence states, and coordination numbers. Operando electrochemical liquid-cell scanning transmission electron microscopy (EC-STEM) will be developed and employed to directly capture time-resolved nanoscale movies of catalyst evolution under controlled temperatures. Operando four-dimensional (4D) STEM will provide unique structural mapping of catalytically active sites at nanometer-to-atomic-scale resolutions by recording a 2D diffraction pattern (crystallographic analysis) over every pixel of a 2D image (atomic positions) in real space. Operando synchrotron-based high-energy-resolution fluorescence-detected (HERFD) X-ray absorption spectroscopy (XAS), with a much higher energy resolution than conventional XAS, will be used to quantify the valence state and coordination environment of a large ensemble of catalytically active sites. This project will elucidate the molecular origin of catalyst evolution and design strategies to accelerate evolution kinetics to increase catalytic activity and enhance catalyst stability. It will also grow atomic-layer Cu on shape-controlled metal nanocrystals and single-crystal electrodes to enhance catalyst stability with strong metal-substrate interaction. Fundamental knowledge learned here will guide the design of active nanostructures, rather than pristine structures, and tune catalyst evolution kinetics for optimal catalytic reactivity and durability under strong electrochemical driving force. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Understanding causal mechanisms is a primary goal of scientific inquiry and an increasingly important objective in modern machine learning and artificial intelligence. In contrast to “classical” causal inference, where the goal is to quantify the causal effect of a pre-specified treatment on a pre-specified outcome, this research will focus on causal discovery, which considers entire complex systems and seeks to identify possible causal relationships among many variables simultaneously. Causal discovery has growing relevance in AI and machine learning (ML) applications that require interpretability, robustness, and scientific reasoning from high-dimensional data. For instance, biologists may apply causal discovery to infer causal structure in intracellular networks, neuroscientists may apply causal discovery to recover causal relationships between brain regions, and ML researchers may use these methods to build more reliable predictive models and foundation models that better capture underlying data-generating processes. Despite many exciting theoretical advances and some promising initial applications, important challenges still hinder the widespread use of causal discovery in empirical research and AI systems. This project will develop theory and methods that broaden the use of causal discovery in the empirical sciences and machine learning, enabling researchers across disciplines to apply these methods in a practical, trustworthy, and reliable manner. Most notably, the research will enable practitioners to rigorously reason about uncertainty in estimated causal structure. The project will also include activities to communicate causal discovery tools to end users in an accessible way, as well as intentional education and training for students at both the undergraduate and graduate levels. The project will primarily consider causal models that can be represented as directed graphs; thus, the goal of causal discovery is to select the causal graph that corresponds to the data-generating mechanism. The research will consist of three main components. The first component will develop methods to compute frequentist confidence sets for causal structure in settings that combine experimental and observational data, including settings commonly encountered in modern machine learning pipelines. These methods will also allow practitioners to adaptively select experiments while maintaining formal coverage guarantees, with potential applications to active learning and sequential decision-making systems. The second component will develop methods that account for uncertainty in the causal graph when estimating a causal effect. When the causal graph is unknown, and the estimand is a specific causal effect, the causal graph is a nuisance parameter. This component will yield methods that rigorously measure the total uncertainty in a causal effect by also accounting for uncertainty in the graph, thereby improving the reliability and interpretability of downstream AI and ML models. Finally, the third component will develop causal discovery methods for data subject to measurement error or generated from mixture models, including high-dimensional and heterogeneous datasets frequently encountered in contemporary AI 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 2026 · 2026-07

The range of users of artificial intelligence (AI) systems has vastly broadened in recent years, and decisions made with the help of AI are now affecting almost everyone. Naturally, the individuals have different preferences over how the AI should behave, which poses new challenges because traditional approaches for developing and deploying AI assume that users have the same preferences. This project builds a theoretical understanding of the blind spots of current AI development and deployment pipelines by studying, for example, when disagreements can make the AI system adopt an unbalanced position or behave erratically. In their place, the project designs new algorithms for the alignment, fine-tuning, and deployment of AI that are provably robust to disagreeing user preferences and strike a sensible compromise between them. To bring the benefits of this research to the public, the project will also produce two software tools: a website comparing AI models to help users choose the right AI tool for their needs and an AI-enhanced tool providing live information on group opinions in participatory processes. The project also develops new courses and educational materials training students in the mathematical foundations of preference-aware AI. The project will achieve these goals by integrating key concepts from computational social choice theory (which provides methods for aggregating the individual group members into a collective decision) and generative AI. First, the project analyzes alignment methods through the lens of distortion, which measures the fraction of choice lost when an algorithm observes only pairwise, stochastic comparisons rather than users' true preferences. This analysis quantifies the shortcomings of the dominant alignment method, reinforcement learning from human feedback, and identifies alternatives with better guarantees. Second, the project designs alignment methods satisfying proportionality guarantees adapted from social choice theory such as core stability, ensuring that every subset of users with aligned preferences receives influence on the AI system commensurate with the group's size. Third, the project develops algorithms for AI-enhanced collective decision processes that select outcomes from open-ended alternative spaces, such as all possible textual summaries, while satisfying strong representation axioms, with a focus on maintaining guarantees even when the AI components fail. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

Scientific discovery increasingly relies on the ability to analyze datasets of unprecedented scale and complexity. In genomics, studies that once examined a few thousand individuals now aim to analyze millions. Computing systems capable of handling this scale exist but using them effectively requires expertise in parallel programming and modern accelerator architectures, which most scientists lack. As a result, important scientific questions remain unanswered because computations would take weeks or months to complete. This award addresses that gap by establishing sparse linear algebra as a unifying abstraction for scientists to express their computations in a standard form, enabling portable execution on evolving computing hardware. By lowering the barrier to advanced computing systems, the project will accelerate discovery in data-intensive sciences while strengthening the United States workforce in high-performance computing. A central component is an undergraduate training program that recruits students from across the United States to participate in international supercomputing competitions, creating a pipeline of talent prepared to use the computing facilities in which the nation has invested. This project develops a systematic methodology for mapping irregular scientific computations onto sparse linear algebra primitives, enabling portable execution across heterogeneous systems, including CPUs, GPUs, and emerging architectures. The project advances three research thrusts. The first develops methods to automatically translate domain code into sparse linear algebra, using a semantic-signature layer that abstracts over syntactic variations of common computational motifs and equality saturation to rewrite code into canonical sparse-primitive form. The second identifies sparsity structure and algebraic provenance from code and uses these properties to specialize sparse primitives. The third extends these application-aware primitives to distributed memory through within-primitive layout derivation and sequence-level composition passes that determine communication-optimal data layout from the recovered structure. The validation includes genome-wide association testing on genotype representation graphs, standard graph algorithm benchmarks, and clustering workloads at scale on national supercomputing resources. The educational activities integrate research outcomes into graduate coursework and create an undergraduate training program to enable students to effectively use supercomputing facilities across the nation. 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 · 2026-06

The epitranscriptome, which encompasses the full profiles of RNA modifications, plays a pivotal role in fine- tuning gene expression. By modulating the functions and metabolism of key components of the translation machinery, including tRNA, ribosome, and mRNA, the epitranscriptome contributes to a broad range of biological processes and is linked to human diseases. Thus, uncovering the function of the epitranscriptome is critical for a comprehensive understanding of the gene expression mechanisms and may lay the foundation for therapeutics. Our overarching research question is how individual organisms evolve their tailored epitranscriptome for optimizing gene expression. Individual organisms have substantial variations in environmental niches and genomic contents. Regardless of such variations, organisms achieve efficient and optimized gene expression. However, how individual organisms establish optimized gene expression is largely unclear. In this proposal, we focus on the epitranscriptome as a mechanism that may contribute to establishing a tailored gene expression system in individual organisms. The epitranscriptome contains ‘universal’ modifications, which are widely conserved in many taxa, and ‘unique’ modifications, which are restricted to a narrow range of species. Our recent studies have discovered novel unique tRNA modifications and their physiological roles, indicating the significance of unique RNA modifications and variations of the epitranscriptome profiles. However, the limited information on RNA modifications in most organisms is a major knowledge gap to understand how individual organisms evolve their epitranscriptome landscape to establish optimal gene expression systems. We will address this knowledge gap with three research areas. We aim to; 1) uncover the chemical structure, modifying enzymes, and functions of unique tRNA modifications in various organisms, 2) unveil the remodeling of the landscape of RNA modifications upon environmental changes in non-model organism, and 3) reveal the epistatic network of tRNA modifications for robust gene expression and resilience to perturbation on translation system across organisms. We have developed a tRNA modification profiling pipeline where we combined next generation sequencing of tRNA (tRNA-seq) with RNA mass spectrometric analysis. This method successfully discovered multiple novel unique tRNA modifications in our published work and preliminary data. Additionally, tRNA-seq enables rapid profiling of tRNA modifications, which easily captures the remodeling of the modification landscape. Collectively, our innovative methodologies, strong preliminary data, and deep expertise in the epitranscriptome research field enable us to address these knowledge gaps. Our results will significantly expand our understanding of how individual organisms tailor their epitranscriptome to fine-tune their gene expression systems.

NIH Research Projects · FY 2026 · 2026-06

ABSTRACT Many human membrane proteins remain understudied, limiting opportunities for the development of novel therapeutics for currently incurable diseases. Our lab aims to uncover the physiological roles and molecular mechanisms of these proteins by combining expertise in structural biology, biochemistry, imaging, and genetics. Using C. elegans as a model organism, we systematically examine protein structure, interactome, expression patterns, and animal behavior associated with these targets. The advantages of C. elegans— including its simple yet highly conserved biology, ease of CRISPR-Cas9 genome editing, large-scale culture compatibility, transparent body for live imaging, and short life cycle—make it an ideal system for these investigations. Supported by NIGMS R01 funding, we have successfully determined the near-atomic resolution structures and elucidated the activation mechanisms of ATP-release channels (pannexins) and initiated studies on NIH-listed understudied membrane proteins linked to rare diseases, demonstrating the effectiveness of our approach. Over the next five years, we will focus on two other understudied membrane proteins with known implications in human diseases but poorly understood physiological roles and molecular mechanisms. Our phased strategy begins with Phase I, where we will generate multiple C. elegans alleles using CRISPR-Cas9 genome editing and extrachromosomal arrays, alongside pre-cryo-EM studies to optimize protein purification conditions. In Phase II, we will conduct functional analyses and structural optimizations, employing label-free quantification mass spectrometry and RNA sequencing to identify signaling pathways and molecular functions associated with these proteins. Structural studies will focus on refining cryo-EM sample quality to ensure high-resolution data. In Phase III, we will leverage endogenously expressed proteins, which preserve their native conformations and complex formations, for advanced cryo- EM studies. Our overarching objective is to elucidate both the structures and molecular functions of these target proteins—a process that often works synergistically. Through our multidisciplinary approach, we aim to provide crucial insights into the physiological roles of these understudied membrane proteins, ultimately paving the way for future therapeutic advancements.

NSF Awards · FY 2026 · 2026-06

Ribosomes are molecular machines that manufacture the proteins needed for cellular life. Ribosomes promote peptide bond formation between amino acids, building polymers which then fold to produce functional proteins. Numerous factors have been discovered that interact with the ribosome and aid protein synthesis. These translation factors can help the ribosome produce amino acid sequences that do not even exist in nature. Therefore, an understanding of how ribosomes produce proteins has important implications for biotechnology, synthetic biology, and advanced materials manufacturing. This project aims to identify proteins that aid translation during stressful conditions in bacteria. During extreme stress, some bacteria can form spores, a type of cell that can remain dormant for thousands of years. Therefore, this work also aims to determine how translation is regulated during spore production. The project includes an educational plan that will engage elementary school students in protein-building workshops at a local science museum. Through hands-on activities involving 3D-printed ribosomes and free software to view protein structures, students are provided an opportunity to learn about molecular biology and protein synthesis. Most of the energy in actively growing cells is dedicated to protein synthesis. Therefore, protein synthesis must be tightly regulated, especially when the cell experiences stressful conditions such as low and high temperatures and nutrient limitation. Using the spore-forming bacterium Bacillus subtilis as a model organism, the proposed work will uncover the functional network of proteins that interact with the ribosome during stress. Proteins of interest include factors involved in ribosome rescue, alarmone production, and translation factors that alter the rate of protein synthesis. Ribosome quality control in sporulating cells will also be investigated since sporulation is an important stress response that requires major changes in genetic programming. By investigating translation in the context of cell development, this proposal will reveal new paradigms in ribosome quality control. 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 · 2026-06

Trajectories of age-related declines in tissue function, cognition, health, and survival are strongly shaped by environmental exposures. Yet, the vast majority of what we know about the biology of aging comes from animals studied under constant, simplified laboratory conditions that poorly reflect the complex environments in which mammalian physiology evolved. This project aims to determine which aspects of aging biology are robust across environments and which are environmentally contingent and at risk of being mischaracterized in the lab. To address this challenge, I will use a naturalistic outdoor enclosure system that allows genetically identical C57BL/6J mice to live in semi-natural social and ecological conditions that much better capture the challenges, complexity, and dynamism of the environments in which mammalian physiology evolved. I have shown that animals reared in these environments experience more rapid epigenetic aging and broad transcriptomic remodeling, indicating that real-world complexity fundamentally alters physiological trajectories. My preliminary data confirm the feasibility and power of this approach, with findings already published in Science and Aging Cell. During the mentored K99 phase, I will expand these findings by testing how environmental realism affects the pace and profile of aging across biological levels—including behavior and cognition, gene expression, DNA methylation, mitochondrial function, and gut microbiome composition. During the R00 phase, I will test whether a leading pro-longevity intervention (rapamycin) is effective under naturalistic conditions. This novel system represents the first sustained effort to bring ecological validity to a tractable aging model, merging evolutionary and biomedical perspectives to examine how real-life complexity influences known hallmarks of aging. This work will identify which biomarkers and interventions retain their relevance outside the lab and will help build a more generalizable foundation for aging biology. Findings will improve the design and interpretation of preclinical studies by revealing context-dependent effects on molecular aging pathways. The proposed career development plan will provide advanced training in molecular and computational tools—including DNA methylation profiling, RNA-seq analysis, mitochondrial functional assays, and microbiome sequencing—critical for my long-term goal of establishing an independent research program at the interface of ecological and biomedical aging research. My training will be supported by a highly collaborative environment at Cornell University, with direct mentorship from Dr. Michael Sheehan, as well as through cross-institutional training from Drs. Wanding Zhou (U. of Pennsylvania), Andrew Moeller (Princeton U.), and Steven Austad (U. of Alabama at Birmingham). Through this integrated training plan, I will gain the interdisciplinary expertise needed to lead a research program that combines molecular gerontology and environmental realism in aging models.

NSF Awards · FY 2026 · 2026-06

Plants capture carbon dioxide from the air through photosynthesis, helping to sustain food production and life on Earth. However, variable temperatures, such as heat waves or cold snaps, threaten this process. This is, in part, because the central carbon-fixing enzyme in plants, Rubisco, performs less efficiently under temperature stress. This CAREER project will investigate how plants naturally adjust Rubisco function when temperatures change, focusing on how they do this by changing which small protein subunits are incorporated into the enzyme. Understanding this process could reveal new strategies for improving photosynthesis in crops, supporting long-term goals in agricultural biotechnology and food security. The project will also advance education and public engagement by training undergraduate students, graduate students, and postdoctoral researchers to communicate the long-term promise of fundamental science through science fiction writing. These activities will help broaden public understanding of how basic biological discoveries can lead to future technologies. The research will determine how temperature-responsive Rubisco small subunits influence carbon-fixation kinetics, enzyme stability, and plant growth. The project will first test this mechanism in Arabidopsis thaliana using Rubisco synthetic biology expression systems, biochemical assays, protein-stability measurements, plant transformation, and whole-plant growth analyses. It will then examine whether similar temperature-dependent Rubisco small-subunit responses occur across diverse land plants, including agriculturally important species, by combining gene-expression analysis, protein-level measurements, comparative sequence analysis, and kinetic characterization of engineered Rubisco variants. Finally, the project will test whether activity-stability tradeoffs can explain how different small subunits tune Rubisco function at different temperatures. By linking natural variation, synthetic biology, protein biochemistry, and plant biotechnology, this work will define molecular principles that may guide future engineering of temperature-adapted carbon fixation in crops. The integrated education plan will support undergraduate research training and develop science communication workshops that culminate in a freely available collection of science fiction stories written by scientists. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Osteocytes are the principal mechanosensors and regulators of bone remodeling, yet they remain an untapped therapeutic target due to their deep embedding within the mineralized bone matrix and their resistance to conventional drug delivery strategies. This project proposes a novel platform for osteocyte-specific therapeutic modulation by leveraging ultrasmall (<10 nm), renally cleared Cornell Dots (C’Dots) and related nanoparticle topologies, engineered to deliver bioactive cargo directly to these deeply embedded cells. Our overarching goal is to develop and validate a new class of nanoparticle-based therapeutics that can activate both extracellular and intracellular Wnt signaling in osteocytes to treat osteoporosis. In Aim 1, we will define how nanoparticle geometry and surface chemistry influence osteocyte uptake and subcellular localization using intravital multiphoton imaging and transmission electron microscopy. In Aim 2, we will test the efficacy of Wnt ligand-functionalized C’Dots in restoring bone formation and mechanosensitivity in healthy and osteoporotic mouse models. In Aim 3, we will advance a transformative intracellular strategy by using TAT-functionalized nanoparticles to deliver intelligently designed peptidesthat stabilize cytosolic β-catenin, mimicking Wnt pathway activation within the osteocyte cytoplasm. These peptides will be selected using high-throughput SPOT epitope mapping from known Wnt-regulatory proteins and validated for intracellular bioactivity. Together, these studies integrate cutting-edge nanotechnology, peptide engineering, and in vivo imaging to establish intracellular osteocyte modulation as a precise and tractable therapeutic strategy. This work has the potential to shift the current paradigm in osteoporosis treatment by directly engaging osteocytes and enabling targeted activation of anabolic signaling pathways previously inaccessible to systemically delivered biologics.

NIH Research Projects · FY 2026 · 2026-05

Project Summary/Abstract Macroautophagy (hereafter ‘autophagy’) is an intracellular degradation process essential for cellular homeostasis and health conserved from yeast to humans. Defects in autophagy are linked to many diseases including neurodegeneration, metabolic disease and cancer. Thus, understanding the mechanisms and physiological functions of autophagy has important and broad implications for human disease and health. Autophagy is characterized by formation of transient double-membrane organelles, termed autophagosomes. Autophagosomes form ‘de novo’ upon fusion of vesicles to nucleate a small single- membrane cisterna called ‘phagophore’ (or isolation membrane). The membrane of the nucleated phagophore then rapidly expands in form of a large cup-shaped structure to enclose cytoplasmic substrates. Upon closure, the phagophore membrane divides into the outer and inner membrane of the double-membrane autophagosome. The outer membrane fuses with the vacuole or lysosome exposing the inner membrane and enclosed substrates to resident hydrolases for degradation and metabolite recycling. The particular mechanisms of autophagosome biogenesis endow cells with an unprecedented capacity for turnover of an unparalleled scope of substrates in size and nature. Our goals in this research proposal are to gain mechanistic and quantitative understanding of the committed regulatory steps in autophagosome biogenesis that control the key parameters of number, size, duration and substrate scope of autophagosomes in response to diverse intracellular and environmental challenges. First, we will analyze the mechanisms underlying autophagosome biogenesis during non- selective autophagy with the goal to develop a general, quantitative and predictive model of autophagosome biogenesis. In this context, we are particularly interested in understanding the mechanisms of phospholipid transfer across specific membrane contact sites. Specifically, we discovered that three conserved bridge-like phospholipid transfer proteins cooperate at the membrane contact site formed between the phagophore and the endoplasmic reticulum to drive the membrane assembly of forming autophagosomes. Second, we aim at understanding how cells modify the mechanisms of autophagosome biogenesis in order to target selective substrates according to specific signal inputs. For this, we will examine the specific mechanisms of autophagosome biogenesis during a homeostatic form of mitophagy, which targets mitochondria specifically for organelle size regulation. Third, we are interested in understanding how cells maintain the membrane composition of mitochondria and how a specific form of mitophagy targets large mitochondria-derived vesicles formed from excess outer mitochondrial membrane. We want to understand how inter- and intraorganellar membrane contact sites are tuned to balance the membrane distribution within mitochondria, drive the biogenesis of outer mitochondrial membrane-derived vesicles and generate autophagosomes for their specific turnover. Our long-term goal is to understand how cells convert complex metabolic, functional and size parameters into finetuned autophagy responses at mechanistic, cellular and physiological level. Deep mechanistic understanding of autophagosome biogenesis has the potential to enable modifying autophagy in a targeted and rational manner to maximize the benefits for a broad spectrum of pathophysiological outcomes.

NIH Research Projects · FY 2026 · 2026-05

Human reproduction is highly error-prone, resulting in aneuploidy (loss or gain of chromosomes), which is a leading cause of early pregnancy loss (EPL). The mechanisms contributing to chromosomal errors remain poorly understood, precluding development of effective diagnostics or therapeutics for prevention of EPL. This proposal aims to identify genetic variants associated with chromosomal abnormalities in early pregnancy loss cases, crucial for developing intervention strategies to mitigate pre-implantation pregnancy loss. I will investigate this from the perspective of centromeres, an essential chromosomal locus that plays a critical role in accurate chromosome segregation. Emerging evidence suggests that genetic and epigenetic (centromere-associated protein A, CENPA) variation at centromeres may contribute to chromosomal instability. However, the impact of centromere variation on aneuploidy risk in pregnancies remains largely unexplored, highlighting a key area of opportunity for understanding the molecular basis of its development and its role in establishing a healthy pregnancy. My preliminary data suggests that aneuploidy affects chromosomes in a non-random manner, a pattern that is conserved between horses and humans. Additionally, I identified large variation in centromere genetics both between individuals, and across chromosomes within the same individual that could drive non- random aneuploidy. Thus, the overarching objective of this proposal is to investigate how large genetic variation at centromeres impacts aneuploidy rates using a unique, naturally occurring animal model of miscarriage: the horse. The genus Equus provides a unique opportunity to test this hypothesis due to natural variation in centromere organization and access to a well-characterized equine pregnancy loss tissue biobank. Aim 1 will generate a comprehensive atlas of equine centromere genetic variation and, importantly, evaluate which specific variants are associated with aneuploid pregnancy loss cases. Aim 2 will explore the relationship between genetic and epigenetic centromere variation and examine the functional impacts of centromere size asymmetry, arising from large variation in underlying genetics, on aneuploidy risk. I will use 1) cell-based models enhancing centromere genetic mismatches to assess impacts of the resulting size asymmetry on aneuploidy, and (2) early equine embryos with known parental centromere differences to directly evaluate aneuploidy risk in natural conceptions. By identifying centromere-linked risk factors for aneuploidy, this work has the potential to inform new diagnostic tools, including genetic tests that assess aneuploidy risk, with implications for couples experiencing recurrent pregnancy loss and IVF failure. This Fellowship will support my research conducted in a multidisciplinary environment, integrating molecular genetics, bioinformatics, and clinical training in reproductive biology, in alignment with goals of the combined DVM-PhD training plan to bridge basic research and clinical applications in reproductive medicine.

NIH Research Projects · FY 2026 · 2026-05

Project Summary/Abstract This proposal requests funds for LifeCanvas Technologies SmartSPIM lightsheet microscope for the Cornell Biotechnology Resource Center (BRC) Imaging Facility, which will enable imaging of optically cleared tissues ten-fold faster as compared to a standard confocal microscope, and will result in reduced photobleaching and a resolution that can be symmetric in all three dimensions. The proposed microscope is designed for large (mm-cm scale) cleared fluorescent tissues, such as whole mouse brains, lymph nodes or ovaries. It will replace a Lavision Biotec (now Miltenyi) Ultramicroscope II, which has become obsolete. The new microscope will enable researchers to better understand cellular or anatomical organization or expression at the organ scale. Data management, scheduling and billing will be accomplished using already established BRC infrastructure. Training will be carried out by a PhD-level staff scientist. The microscope will be available to all researchers on and off campus, and will advance a broad segment of NIH-funded research programs by ensuring access to multicolor lightsheet excitation with macro immersion optics that can accommodate cm-scale tissue pieces.

NSF Awards · FY 2026 · 2026-05

The immune system is the primary defense animals have against infectious diseases. A key component of this defense, the adaptive immune system, generates antibodies that recognize and neutralize bacteria, viruses, and other pathogens. The repertoire of antibodies an animal can produce is determined by a set of genes that vary considerably across mammalian species, yet this variation is almost poorly describe outside of humans, mice, and cattle. This project will conduct the first systematic comparison of immune genes and antibodies across 60 species of mammals, using state-of-the-art DNA and RNA sequencing technologies. The research team will generate high-quality genome assemblies focused on immune-related genes and expressed antibody repertoires, develop new computational tools to analyze them, and identify patterns that explain why species differ in their capacity to respond to infection. All data, genome assemblies, and software produced by this project will be made freely and publicly available, providing a foundational resource for immunology and disease research for years to come with the potential to facilitate biotechnological advances. The project will also provide meaningful research training for graduate and undergraduate students in computational biology, genetics, and immunology. Graduate students will gain hands-on experience with both laboratory sequencing methods and advanced computational analysis, while undergraduate students will participate in paid summer research positions. The team will also host a working group to establish community standards for comparing immune gene data across species, laying the groundwork for a broader research consortium that will expand this work well beyond the lifetime of this grant and further facilitate biotechnology advances. This project will generate paired datasets of antibody repertoires and germline immunoglobulin (IG) loci across approximately 60 mammalian species. For each species, expressed antibody repertoires will be profiled using long-read bulk RNA sequencing (PacBio Iso-Seq, a technology enabling full-length transcript recovery) of whole blood samples, enabling unbiased identification of V(D)J recombinations, the combinatorial gene rearrangements that generate antibody diversity, across all antibody chains. In parallel, high-quality genome assemblies will be generated using long-read whole-genome sequencing (PacBio HiFi), and IG loci will be assembled using state-of-the-art assemblers followed by targeted quality evaluation. New computational methods will be developed to integrate repertoire and genomic data to: (i) improve detection and annotation of germline IG genes, including highly divergent and previously unrecognized gene families; (ii) identify non-canonical antibody features such as ultralong or structurally atypical antigen-binding sites; and (iii) characterize structural variation and gene organization within IG loci. Using these data, the project will quantify species-level variation in germline gene content, gene usage frequencies, and V(D)J recombination features. Phylogenetic comparative models will then be applied to test hypotheses linking variation within antibody repertoires to ecological and life-history variables (including lifespan, diet, and population dynamics) and to reconstruct the evolutionary history of IG gene families. Finally, the project will analyze relationships between germline IG gene copy number, genomic organization, and expression bias to assess how the molecular evolution of IG loci shapes antibody repertoires. 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 · 2026-05

SUMMARY The b-lactam antibiotics (penicillin and related compounds) are among the strongest weapons in our antimicrobial arsenal, due to their ability to effectively induce lysis and thus death in many bacterial species. Despite their importance and concomitant decades of intense research efforts, we do not understand how exactly these drugs kill bacteria, though some clues exist. b-lactams inhibit penicillin-binding proteins (PBPs), the principal cell wall synthases. PBP inhibition leads to cell wall degradation, likely via the poorly-understood dysregulated action of so-called “autolysins”, a diverse group of endogenous cell wall cleavage enzymes. Research efforts into the molecular details of b-lactam induced cell wall breakdown have traditionally been stymied by the absence of appropriate tools to quantitatively and qualitatively analyze cell wall breakdown products consisting of peptidoglycan (PG) fragments, and by the dynamic situation of analyzing a cell during the process of its lysis. Here, we propose to use a novel PG architecture analysis pipeline to assess cell wall turnover in a high-throughput fashion. We have also established new bacterial model systems that recapitulate b-lactam- induced cell wall degradation while retaining structural integrity, solving the complication of using lysing cells for PG architecture analysis. Using these tools, we will comprehensively interrogate downstream consequences of b-lactam exposure, determine the contributions of specific autolysins to the cell wall turnover process and map out new pathways and environmental conditions that modulate b-lactam mechanism of action. At the end of the project, we will generate a model that functionally integrates environmental cues, intrinsic metabolic functions and autolytic potential to finally fully understand b-lactam mechanism of action. Such an understanding will fuel the development of novel antibiotic adjuvants that optimize the effectiveness of b-lactams by exploiting key pathways that determine b-lactam-induced structural integrity failure.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Hormones pleiotropically regulate diverse physiological processes, often through tiered gene regulatory networks (GRNs) which consist of primary response genes that are directly regulated by the hormone and secondary response genes that are regulated by transcription factors in the primary response set. A central hypothesis in this proposal is that pleiotropic hormones can enact distinct gene regulatory programs via hierarchically tiered gene regulation. It is further hypothesized that modest differences in the primary level of regulation can result in larger differences at the secondary level, leading to functionally specialized GRNs over development and in response to environmental stimulus. However, distinguishing primary from secondary regulation is difficult or impossible from transcriptomic data alone, presenting a major hindrance to deciphering how hormones regulate pleiotropic balances. This project will overcome that challenge by combining transcriptomic profiling with computational analysis and direct assays of DNA-binding to decipher primary and secondary levels of gene regulation. The project will focus on the hormone 20- hydroxyecdysone (or ecdysone, 20E) in the Drosophila melanogaster model system. 20E has well-defined roles in development and a documented but undefined role in regulating the immune system. In Aim 1, an ex vivo tissue assay will be used to build the 20E gene-regulatory network. Transcriptomic analysis, measurement of direct DNA binding by the nuclear hormone receptor, and characterization of the chromatin landscape will be combined to identify primary targets of 20E signaling. Transcription factors in the primary-regulated gene set will then be computationally and experimentally evaluated for regulation of genes in the secondary level of control. In Aim 2, experimental infections with pathogenic bacteria will be used to define the 20E modulation of the immune system in vivo. Both 20E and innate immune signaling are highly conserved across insects, including in agricultural pests and human disease vectors, and the endocrine-mediated development-immunity pleiotropy can be exploited for insect control mechanisms to promote food security and improve public health. More broadly, the principles hormonal regulation of immune function are likely to be conserved across all animals, including humans, and pleiotropic endocrine GRNs allow evolutionary adaptation in life history balances. The methodology and workflow developed in this proposal can be applied broadly to hierarchical gene regulatory networks in any system.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Antimicrobial peptides (AMPs) form the first line of defense against opportunistic bacterial infections, and they hold promise as potential therapeutic compounds. This project will test several hypotheses of AMP function and evolution through a combination of functional genetic, evolutionary, and phenotypic analyses. The project will focus on insect AMPs in order to take advantage of the experimental power and flexibility of the Drosophila melanogaster model system, but with the intention of determining general principles of AMP function, evolution and engineering. Whereas AMPs were previously believed to have broad-spectrum antibiotic activity, new evidence suggests the potential for high degrees of specificity and coevolution with environmentally prevalent microbes. Three classes of AMP will be evaluated for antibacterial specificity and synergistic interactions in vitro and in vivo against a panel of Gram-negative opportunistic pathogens that are chosen for their human clinical relevance and ecological relevance to D. melanogaster. The population genetics of these AMPs will be assessed within a population of Drosophila melanogaster to test for adaptive maintenance of functionally relevant polymorphism, and the molecular evolution of the gene families will be assessed over the Drosophilid phylogenetic tree to test for adaptive diversification in response to distinct ecological pressures. Genotype-phenotype association mapping of D. melanogaster allelic variants with resistance to 8 opportunistic pathogen species will reveal crucial functional residues and peptide domains, and molecular evolutionary analyses will be performed to identify regions of the peptides that may co-evolve with environmental pathogens. Bacterial genetic screening will be employed to identify the molecular targets of two AMPs, Diptericin and Attacin, that have been demonstrated in preliminary data to have high specific activity against Providencia rettgeri and Serratia marcescens, respectively. Ultimately, the structural, functional, and evolutionary understanding of the AMPs will be integrated in the synthesis of novel AMPs that have engineered antibacterial specificity against chosen pathogens.

NIH Research Projects · FY 2026 · 2026-05

Project Summary/Abstract Cells continuously synthesize, degrade, and redistribute phospholipids. While vesicular trafficking was once considered the mechanism of this redistribution, recent studies have highlighted the critical roles of lipid transfer proteins (LTPs) in various contexts. For example, large quantities of phospholipids must be supplied to repair damaged lysosomal membranes or to form autophagosomes during starvation. However, it remains unclear why cells expend substantial energy to transport phospholipids even under normal physiological conditions. A major barrier to addressing this question has been the lack of methods to directly and comprehensively measure phospholipid transport in vivo without introducing bulky tags that perturb native lipid behavior. To circumvent this challenge, Aim 1 will establish a method to comprehensively profile inter-organelle phospholipid transport, referred to here as a “fluxome profile,” starting with phosphatidylcholine (PC). Using phospholipase D (PLD), I will perform organelle-specific pulse labeling by converting PC into deuterated PC (d- PC). After a chase period, d-PC distribution will be measured by stimulated Raman scattering (SRS) imaging, which detects vibrational modes of subcellular chemical bonds as well as the carbon–deuterium bond. This minimally perturbative label enables the observation of natural PC transport under physiological conditions. In parallel, I will create a “lipoprint map” by reclassifying organelles based on lipid-derived SRS spectral fingerprints. By projecting d-PC pulse-chase data from various organelles onto this map, I will obtain the fluxome profile, a comprehensive landscape of inter-organelle relationships defined by lipid composition and transport dynamics. In Aim 2, I will extend this strategy to other phospholipid classes by engineering PLD to alter its substrate specificity. This will involve three approaches: (i) an in vivo strategy that converts cellular PLD activities toward two different substrates into spectrally separated fluorescent signals for high-throughput screening; (ii) an in vitro method to pulldown PLD by phospholipids to enrich PLD sequences with desired substrate specificity; and (iii) an in silico deep learning model that predicts PLD activity for virtual screening. These approaches will enable selective deuterium labeling of major phospholipid classes and allow construction of fluxome profiles beyond PC. In Aim 3, I will first establish a method for the absolute quantification of organelle phospholipids. These data will enable quantitative comparison of phospholipid transport when different organelles serve as the pulse- labeling origin, providing the basis for a quantitative network model of inter-organelle phospholipid transport. I will then assess the structural robustness of this network model and experimentally validate it through LTP overexpression or knockout and engineered PLD-based perturbations. These analyses will test the hypothesis that the lipid transport network alone is a sufficient and efficient mechanism for maintaining organelle phospholipid compositions, uncovering fundamental design principles by which cells preserve membrane homeostasis under normal physiological conditions.

NSF Awards · FY 2026 · 2026-05

The 45th Conference on Stochastic Processes and Their Applications will take place at Cornell University, Ithaca, NY, USA, from June 14 to 20, 2026. Organized under the auspices of the Bernoulli Society for Mathematical Statistics and Probability with the support of the Institute for Mathematical Statistics, it is the major international annual conference on Probability theory. This annual conference is a key vector of new advances and provides participants the opportunity to stay abreast of the latest developments in this wide ranging and very important area of mathematics. The conference will bring to Ithaca, NY, the worldwide leaders of the field of Stochastic Processes and Their Applications. They will present their most recent results and organize invited sessions on the most current topics of interest in probability theory. These subjects cover a wide variety of topics from quantum gravity, the Gaussian free field, random environments, Lattice Gauge theory, the geometry of random fields, and much more. The website for the conference is: https://events.ces.scl.cornell.edu/event/spa2026/summary 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 · 2026-05

Project Summary Mitochondrial function and cellular energy production are influenced strongly by maintenance of mitochondrial DNA (mtDNA). mtDNA depletion syndromes (MDS) result from inborn errors of metabolism (IEM) in mtDNA replication and repair enzymes. MDS are characterized by impaired mtDNA synthesis, mtDNA deletions, mitochondrial dysfunction, and severe multi-organ dysfunction phenotypes. However, the severity of the clinical features of MDS vary widely. There is also considerable heterogeneity in both the clinical presentation and age at onset for many MDS, even among individuals presenting with the same genetic mutations; this heterogeneity is believed to be driven by environmental and nutritional exposures. Maintenance of cellular thymidylate (dTMP) pools is essential for accurate DNA replication and for maintaining integrity of both mtDNA and nuclear DNA. Vitamin B12 (B12) is an essential cofactor required for de novo dTMP synthesis. B12 deficiency impairs de novo dTMP synthesis, leading to loss of DNA replication/repair fidelity and DNA damage. Our preliminary data indicates that mtDNA is more sensitive to B12 deficiency than is nuclear DNA and that B12 deficiency causes mtDNA damage, which then impairs mitochondrial energy production. MtDNA damage and impaired energy production are hallmarks of MDS. Understanding the role of B12 in MDS is important because B12 deficiency is common in older adults, vegans/vegetarians, and is a side-effect of commonly prescribed pharmaceuticals. Our central hypothesis is that B12 deficiency acts as a “second hit” to further impair mtDNA stability and exacerbate mitochondrial impairment and energy production in MDS. Ultimately, we hypothesize that B12 deficiency contributes to heterogeneity of onset and clinical presentation in MDS. The principal objectives of the proposed work are to: 1) define molecular mechanisms whereby B12 deficiency affects mtDNA stability biomarkers, mitochondrial function, and muscle strength in a mouse model of MDS, and 2) determine the role of B12 supplementation in mitigating adverse outcomes in MDS. Because mtDNA integrity declines with aging, the findings are likely to be relevant not only to individuals with IEM leading to MDS but also to older individuals and those affected by chronic disease.

NSF Awards · FY 2026 · 2026-05

Quantum computing has the potential to revolutionize industries such as cryptography, pharmaceuticals, and finance by solving complex problems that are beyond the capabilities of classical computers. However, scaling quantum processors to control and read out thousands or even millions of qubits is a significant challenge due to limitations in current technologies, such as the use of bulky coaxial cables and inefficient control systems. This project aims to overcome these barriers by developing innovative, energy-efficient, and scalable interfaces for cryogenically cooled quantum processors. By addressing the input/output bottleneck, the project seeks to accelerate the development of quantum processors, bringing us closer to achieving quantum supremacy and enabling transformative advancements in computing, secure communications, and sensing technologies. The project also includes an education and outreach plan to engage high school and undergraduate students, modernize curricula, and create open-source software to broaden participation in quantum-related research. These efforts aim to address the critical need for skilled professionals in quantum technology and microelectronics, ensuring the U.S. remains a global leader in these fields. The project focuses on designing and demonstrating three key innovations for scalable cryogenic quantum systems. First, it will develop multiplexed all-passive photonic ingress interfaces using wavelength-division multiplexing (WDM) systems and cryogenic receivers to directly control qubits with minimal heat load and high energy efficiency. Second, it will create photonic ingress data links for hybrid integration with novel qubit controllers, enabling efficient delivery of digital signals and reducing memory access overhead. Third, it will design sub-THz dielectric-waveguide-based egress interfaces for multiplexed qubit readout, leveraging low-loss polymer-based waveguides to upconvert readout signals to sub-THz carriers for scalable data transmission. The project will utilize advanced semiconductor fabrication technologies and will integrate cryogenic and room-temperature systems for full demonstrations. These innovations promise to set new benchmarks for scalable qubit control, transforming quantum computing and enabling its widespread adoption across industries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY The goal of this exploratory work is to test the potentially transformative hypothesis that fungi harbor a common developmental program for bacterial endosymbiont accommodation (BEA). In the process, we will build foundations for elucidating the principles that govern the establishment and maintenance of associations between the Mucoromycetes fungi (MUC) and endosymbiotic bacteria (EB), which are frequently found in this group of fungi. We expect that these insights will inform future therapeutic strategies for treatment of mucormycoses, which are increasingly frequent, highly destructive, and often fatal in immunocompromised individuals. While bacteria-free asymbiotic non-host MUC are responsible for many cases of mucormycosis, EB are often detected in clinical isolates and known to affect fungal virulence in humans. Therefore, elucidating factors to that govern establishment and maintenance of MUC-EB symbioses is important for developing novel therapies aimed at eradication of MUC infections. Our previous studies of mutualistic and antagonistic interactions between MUC and bacteria revealed candidate fungal pattern recognition receptors and symbiosis regulators responsible for perception of bacterial signals and either mutualistic partner accommodation by host fungi or defense against bacteria perceived as antagonists by non-host fungi. We also found that, in addition to live bacteria, fungal innate immunity responses are activated by bacterial cellular components known as microbe-associated molecular patterns (MAMPs). Similarities shared by fungal-bacterial interactions with host-microbe interactions of plants and animals in model mutualisms and antagonisms inspired our central hypothesis that, like plants and animals, fungi possess an ancient BEA program to modulate bacteria-activated innate immune responses and accommodate beneficial endobacteria. To advance our overall goal, we will: (1) describe the full range of fungal responses to bacteria by conducting transcriptional profiling and biochemical assays of symbiotic host, aposymbiotic host and asymbiotic non-host Rm responses to bacterial MAMPs and live Myc and (2) characterize candidate fungal receptors of bacterial signals as well as investigate candidate regulators and executors of the hypothesized BEA program by generating disruption mutants and examining their responses to mutualistic versus antagonistic bacteria.. Expected outcomes of the project include characterization of fungal proteins and pathways involved in innate immunity and endosymbiont accommodation that could be disrupted for targeted in future drug development.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY Serotonergic psychedelics, such as psilocybin, are known for profoundly altering visual perception, experienced through both elementary and complex hallucinations. But the mechanism by which psychedelics alter visual information processing remains unclear. I hypothesize that psychedelic-driven changes in microcircuit activity in the primary visual cortex may underlie the visual state that accompanies a psychedelic experience. This proposal aims to determine psilocybin’s cellular and microcircuit mechanisms of action on parvalbumin interneurons in the mouse primary visual cortex. In Aim 1, I will measure the effects of psilocybin on spontaneous and evoked activity of both parvalbumin interneurons and excitatory cells. I will use large-scale in vivo Neuropixel recordings and optogenetic cell-type tagging to measure their firing rates before and after psilocybin treatment. In Preliminary Results, I found psilocybin-induced increases in spontaneous firing activity of parvalbumin interneurons. Evoked activity will be measured with stimuli of varying contrasts and orientations to characterize effects on elementary visual processing. In Aim 2, I will examine whether 5-HT2AR serotonin receptors, expressed in the parvalbumin interneurons in the primary visual cortex, mediate the parvalbumin cell responsiveness to psilocybin. To accomplish this, I will create and record from PV-specific 5-HT2AR knockouts, generated via an inducible Cre strategy. The proposed experiments are designed to elucidate how psilocybin disrupts early visual processing and thus perception. In doing so, I hope to advance our knowledge of psychedelic action on vision and open the way for greater insight into hallucinations. In the future, by identifying the microcircuitry responses to psilocybin treatment, targeted drugs may be designed to reduce or eliminate visual side effects while maintaining long- lasting antidepressant effects.