New York University

NIH Research Projects · FY 2026 · 2025-06

Project Summary In planning and carrying our actions we have remarkable flexibility. We can change from sprinting to carefully stepping when we encounter an icy path, and we can immediately turn around based on ongoing thoughts, such as remembering that we forgot our keys. This constant interplay between our thoughts and our actions breaks down in neurologic and psychiatric disorders. For example, patients with Parkinson’s disease can often quickly walk in a straight line but are unable to turn around at will. However, the glue that binds cognitive and motor processes is unknown, and treatments for neuropsychiatric disorders target movement or cognition separately. In this proposal, we take a different approach – we focus on how brain structures essential for movement bind our thoughts and actions. This proposal seeks to record and stimulate the basal ganglia output nodes and motor thalamus, a group of interconnected deep brain nuclei thought to gate movement initiation. We perform our experiments in awake human subjects undergoing implantation of deep brain stimulation (DBS) electrodes. Subjects perform a decision-making task in which they judge the identity or expression of faces, reporting their choice and confidence about the choice with a reaching movement. In Aim 1, we show that the neural responses in the output nodes of basal ganglia and motor thalamus are not limited to encoding movement initiation. We will test the diversity of neural responses, especially co- encoding of movement kinematics with the confidence, outcome, and reward prediction errors associated with the movement. In Aim 2, we show that subjects adapt their decision strategy based on the history of past actions, confidence, and outcomes. We will test whether these factors are integrated by the neural responses across trials, and whether neural responses in the output nodes of basal ganglia and motor thalamus mediate adjustments of decisions strategy. In Aim 3, we will causally test the contribution of the neural responses in these regions to behavioral adjustments by disrupting the neural activity during precisely-times behavioral periods in this task. Our experiments will determine how the link between ongoing actions and thoughts influences future behavior and how this can be modified. The goal is to use the insights from this research to ultimately develop brain stimulation strategies that treat movement and cognitive deficits in neuropsychiatric disorders as two sides of the same coin.

NIH Research Projects · FY 2025 · 2025-06

PROJECT SUMMARY The nucleus accumbens (NAc) is part of the ventral striatum, plays a key role in motivated behaviors, and is disrupted in substance use disorder. Almost all striatal cells are GABAergic, with the exception of cholinergic interneurons (CINs), which represent only 1% of the total population. CINs are the only source of acetylcholine in the striatum, which regulates other cell types, synaptic connections, and neuromodulators. Recent studies have highlighted the importance of CINs and acetylcholine modulation in both the function and dysfunction of the NAc. However, most of what we know about the local and long-range connections onto these cells comes from work in the dorsal striatum. For example, cortical and thalamic inputs show markedly different short-term dynamics and have distinct influences on the firing of CINs. Recent work from our lab in the NAc medial shell (NAcMS) shows CINs receive very different long-range excitatory and local inhibitory inputs. For example, hippocampal and thalamic inputs are excitatory, but can also activate local interneurons that mediate feed- forward inhibition. However, little is known about the equivalent connectivity onto CINs in the NAc core (NAcCore), which has different behavioral roles and distinct inputs and outputs. Here, we characterize how diverse excitatory and inhibitory inputs contact CINs in the NAcCore and influence their firing. We use a combination of anatomy, slice electrophysiology, and optogenetics to examine synaptic connectivity and function in the mouse brain. In Aim 1, we use cell-type specific retrograde anatomy to identify which cells in the local circuit and other brain regions synapse onto CINs. Our preliminary data indicate a variety of input structures, including long-range inputs from prefrontal cortex, midline thalamus, and ventral pallidum. In Aim 2, we use slice electrophysiology and optogenetics to focus on how long-range inputs contact and influence CINs. Our preliminary data suggest long-range inputs can have markedly different properties, including cortical and thalamic excitation, as well as ventral pallidal inhibition. In Aim 3, we explore how long-range excitatory inputs engage GABAergic interneurons that in turn contact CINs and mediate feed-forward inhibition. Our recent results suggest that local inhibitory circuits can be very different between the dorsal and ventral striatum. Together, our experiments will provide important information about how CINs integrate and process a variety of excitatory and inhibitory inputs. Our results will help explain how this important class of interneurons participates in their local and long-range circuits to guide motivated behaviors and become disrupted in mental health disorders.

NSF Awards · FY 2025 · 2025-06

This REU Site award to the Center for Neural Science, New York University, located in New York City, NY, will support the training of 10 students for 10 weeks during the summers of 2025-2027. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities, will be trained in the program and contribute to development of the US STEM workforce. The scientific focus will be neuroscience, spanning the topics of cognition, decision-making, perception, neural control of movement, development of the nervous system, neural basis of social interactions, learning and memory. To prepare students for research, they will participate in a one-week bootcamp, to learn fundamental skills used across many neuroscience laboratories. For professional development, students will read scientific literature and become trained in scientific writing and oral communication. Through these activities, students will learn how research is conducted, and all will present the results of their work at scientific conferences. The effectiveness of the REU site will be assessed using student feedback and tracking the career path and publication record of program participants. Students should apply to the REU site using NSF ETAP (Education and Training Application: https://etap.nsf.gov). This REU Site in Neural Science at NYU will provide neuroscience skills to enhance preparation for pursuing graduate-level research or other STEM careers. Students will be matched to a lab aligned with their own interests to conduct hypothesis driven research that uses molecular, computational, and developmental approaches for understanding perception, cognition, learning, memory, and decision making. Research will involve cutting-edge training using immunocytochemical, biochemical, biophysical and molecular approaches to analyze neural samples. Techniques applied in the research will include neurophysiology, brain imaging, behavioral analyses using specialized imaging equipment, MRI, and in vivo recording of brain activity. Professional development activities will train students in scientific literature reading, science communication skills, research ethics, lab safety, and general lab skills. This site is supported by the Department of Defense in partnership with the NSF REU program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project constructs a new dataset from county land-use records. The dataset includes information on land records from 20 counties spread across the United States over 40 years. It includes data on the Federal Housing Administration (FHA) insured and Veteran’s Administration (VA) guaranteed mortgages in these counties. The data will be publicly available to academics, decision makers, community organizations, and individuals who want to evaluate the impact of these two important federal programs. The team is also providing an initial analysis of these data to describe and analyze the spatial and demographic patterns of federal mortgage insurance. This analysis leverages individual-level borrower data and address-level property information to examine how these programs affected homeownership, wealth, and neighborhood outcomes in both urban and rural counties with different population characteristics. The research findings have the potential to improve the functioning of mortgage markets, thereby enhancing the well-being of U.S. households. The award is jointly funded by the NSF programs in Economics, Sociology, and Human-Environment and Geographical Sciences (HEGS). The project creates a new data resource that provides the most extensive and granular data on the demographic and spatial distributions of FHA-insured and VA-guaranteed mortgages to date. Because the data include information on issued mortgages, the data allow scientists to consider the results of enacted policy rather than simply examining government agency reports and correspondence. The team is collecting and geocoding data on roughly 280,000 mortgages. They are using the data and econometric methods to provide detailed descriptive statistics on the recipients of government-insured mortgages. They also use the data to test hypotheses about the effects of FHA and VA loans on neighborhood composition, including information on differences across population groups and neighborhoods. The project advances knowledge in economics, sociology, and geography. The broader impacts include access to data and enabling science-informed discussions about issues that affect wealth accumulation, neighborhood outcomes, intergenerational mobility, and demographic differences. The project also involves students and early career researchers in data collection and analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Among all shapes that enclose a given volume, which ones have the smallest possible surface area? This classical question, known as the isoperimetric problem, dates back to ancient Greece and appears naturally in phenomena such as soap bubbles, crystals, and models of the universe in physics. This project aims to deepen our understanding of how geometric properties of a space -- especially curvature -- influence solutions to geometric optimization problems. The broader impact of the project includes mentoring undergraduate and graduate students and organizing seminars to engage the local mathematical community and foster the development of young researchers. The project uses tools from Geometric Measure Theory to study the isoperimetric structure of spaces with geometric constraints. A central theme is the development of a theory for spaces with curvature bounded below in a spectral sense, with applications to classical problems like the classification of stable minimal surfaces in Euclidean space. Another major goal is to analyze the existence and uniqueness of isoperimetric sets in nonnegatively curved spaces, including specific cases like the Euclidean unit cube and manifolds with nonnegative scalar curvature -- a setting that is also relevant in Mathematical Relativity. The project also addresses problems at the intersection of Algebra, Analysis, and Geometry, including the rectifiability of metric spaces with the same tangents almost everywhere, and the quasi-isometric classification of nilpotent groups. 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 · 2025-05

Project Summary Longitudinal cohort studies are a rich resource for estimation and modeling of Alzheimer's disease (AD) progression. Datasets extracted from these studies often feature complex truncation (selection) and censoring. Estimates based on these datasets are used for (1) clinical trial design; (2) improved understanding of AD progression, risk and prevention; and (3) individual prediction, but do not fully account for the complex truncation. This proposal develops methods that make proper adjustments and thereby enhance each of these essential needs in AD. Use of time-to-event endpoints in clinical trials for AD is supported by regulatory authorities when the time origin and the event are clinically meaningful, e.g., onset of cognitive decline and mild cognitive impairment (MCI). Time-to-event estimation and modeling are needed to support trial design (e.g., numbers of events and participants, length of follow-up, eligibility). These estimates are commonly based on subcohorts from large longitudinal cohort studies, which feature complex truncation and censoring. Current estimates use a time origin of convenience, such as study entry, rather than a clinically meaningful time origin, as required by clinical trials. Aim 1 develops methods for estimation and regression that enable use of clinically relevant time origins through proper adjustment for fully and partially sequential truncation in longitudinal cohort studies. Unbiased estimates of associations between measures of interest in studies of AD such as blood biomarker levels or amyloid imaging levels and factors such as maternal age at dementia onset or other biomarker values are important for the evolving understanding of AD progression, which informs drug development and treatment and personalized risk assessment. Many published regression analyses ignore the censoring and “cure” (i.e., unobserved latent classes) of these factors and obtain biased associations. Aim 2 develops methods that adjust for covariates that are subject to censoring or cure and yields accurate estimates of association. Accurate predictions of clinically meaningful times-to-event that adjust for subject-specific features are essential to provide prognostic guidance to patients. Conformal prediction provides a flexible framework for quantifying uncertainty based on the use of arbitrary prediction algorithms. Conformal prediction targets the actual event time itself, which is the most meaningful measure for patients. However, it has not been developed for settings of truncation (Aim 1) or incompletely observed covariates (Aim 2). Aim 3 fills these gaps and will enable use of conformal prediction in the setting of times-to-events that are clinically meaningful in AD progression based on longitudinal cohort studies.

NSF Awards · FY 2025 · 2025-05

This EArly-Concept Grant for Exploratory Research (EAGER) will fund research that investigates the following question: can removing obstacles to data regulation practices for auto insurance improve road safety? Though it has long been known that the accident externality of driving, which refers to potential negative consequences from driving or having a vehicle on the road that affect other people, is significant and not adequately priced, remediating the issue has been challenging. Connectivity has provided both insurers and automakers with an opportunity to price driving risk more effectively by monitoring driver behavior. However, as presently constructed and regulated, auto insurance monitoring programs do not allow for a full use of individual driving data to price insurance. At the root of this missed opportunity to induce safer roads lie concerns related to data use and privacy. A structure that collects data on driver behavior and shares the data with specific automakers and insurers with drivers’ consent could potentially alleviate these privacy concerns while making roads safer. However, to which extent could such a structure contribute meaningfully to increasing road safety? What would the impact be of such a structure on the auto insurance industry? And to which extent would it affect the car manufacturing industry and reshape the conversation around automated driving? Through a series of theoretical and empirical analyses and by engaging the relevant stakeholders, this research will seek to provide answers to these questions. The research will lay the foundation to help inform transportation industry policy making and better road safety management. The research activities will be integrated into teaching and course design, as well as outreach activities for broadening participation in STEM. Research discoveries will be further disseminated through conferences and collaborations with practitioners. The research centers on two main tasks. First, a mathematical model that captures the impact of information availability on insurance pricing, driver behavior, and aggregate accident probability will be developed. This model will be used to study equilibria that emerge on a road or in a road network when more information about driving behavior is shared. Additionally, market structure, incentives for technology adoption, and the interactions between automakers and insurers will be considered. The outcome of these analyses will be a characterization of an upper bound on the benefits of a more holistic use of driving data in insurance pricing. Second, a series of interviews with different industry stakeholders will provide insights on attitudes towards removing obstacles to a wider use of driving behavior data in auto insurance. Together, the research will help inform policymaking and data regulations in auto insurance and road safety in the era of connected and automated vehicles. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Recent advances in neurotechnology, driven by neural interfaces, have dramatically enhanced our understanding of brain function. These breakthroughs are laying the groundwork for next-generation neural prosthetic systems and innovative treatments for neurological disorders. Real-time neural monitoring across a large number of neurons is essential to achieving this vision, with future advancements in neuroscience relying on the ability to acquire high-resolution neural data from single neurons. On the other hand, clinical requirements for neural interfaces continue to demand compact wireless devices capable of long-term in-vivo operation to address potential safety concerns. Despite significant progress in high-resolution neural probes, current limitations in wireless connectivity, power delivery, and signal acquisition circuitry (particularly with respect to energy efficiency and hardware integration) have hindered the scalability of neural interfaces capable of recording from thousands of channels simultaneously. This project seeks to revolutionize neural interfaces by tackling the above challenges associated with powering, communication, and data acquisition for brain-machine interface systems. It will focus on developing innovative wireless systems that combine advanced wireless power transfer methods, energy-efficient and high-bandwidth integrated circuits, and large-scale neural data recording capabilities. In addition to its technical objectives, the project promotes education and outreach through the development of new curricula, mentorship programs, and interactive hands-on workshops. By integrating advancements in bioelectronics, system design, and educational initiatives, this project has the potential to strengthen U.S. leadership in the semiconductor industry while contributing significantly to public welfare. The overarching goal of this CAREER project is to advance the theoretical and engineering foundations of wireless neural interfaces. This goal will be achieved through the development of wirelessly powered distributed integrated systems and by fundamentally improving power delivery, energy efficiency, wireless communication data throughput, and the number of simultaneous neural recording channels by an order of magnitude. Adopting a holistic system-on-chip (SoC) design framework, this project includes three interlocked research thrusts. First, it will explore a novel architecture for wireless power transmission to miniaturized implants using programmable near-field electromagnetic fields. The architecture will employ multiple focused power beams to increase received power without the risk of increasing thermal absorption in biological tissues. Second, it aims to scale communication bandwidth through antenna-circuit codesign and high-order modulation schemes, enabling real-time data transmission with minimal latency under stringent power and form-factor constraints. Finally, it will develop a large-scale neural electrode array and high-count readout circuitry with a novel readout/routing-sharing architecture. This approach reduces the number of amplifiers and interconnects required for high-channel-count neural recording interfaces at ultrafine scales. The research project employs a cross-disciplinary methodology, ranging from theoretical development to experimental demonstrations. It encompasses analysis from Maxwell's equations to antennas and metasurfaces, as well as integrated circuit design, alongside extensive simulations and experiments. The project aims to develop new knowledge in the area of implanted electronics and seeks the development of miniaturized batteryless implants with unprecedented performance, ultimately establishing the foundation of the next-generation neurotechnology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Archaeologists continue to debate when and how humans' use of dense and predictable resources impacted human evolution. Today, billions of humans still depend on dense and predictable resources, namely agriculture. Previous studies suggest that agriculture allowed humans to become sedentary, leading to greater technological advancements and forms of resource defense and stratification. Recent archaeological evidence suggests these adaptations predate agricultural development and, thus, may be more ancient than previously thought. However, before archaeologists can determine how far back in time these adaptations exist, they must first understand how other forms of dense and predictable resources, like fish and shellfish, impacted hunter-gatherer behaviors. As such, this project yields insights into the role of aquatic resources in human evolution, providing comparative data for global studies of hunter-gatherer adaptation. The project supports education and training by involving students and engaging local communities while fostering scientific collaboration. Educational outreach, including the development of language material and a database of archaeological data on aquatic exploitation, extends the impact of this work to a variety of audiences and educational settings. This interdisciplinary project contributes to theoretical and methodological advancements in archaeology and anthropology while enriching understanding of past, present, and future human responses to resource challenges. Researchers investigate how intensified aquatic resource use influenced hunter-gatherers' technological and mobility decisions over the past 5,000 years. This study examines stone tool adaptations across two distinct environments. Both sites provide rich archaeological records of transitions towards increased consumption of fish or shellfish, enabling the researchers to analyze the impact of aquatic intensification on hunter-gatherer technology and mobility. This project tracks how hunter-gatherers made, maintained, and sourced their stone tools before and during aquatic intensification. This allows the researchers to explore whether shifts in dietary focus—toward marine and freshwater resources—altered hunter-gatherer strategies for tool utility, raw material use, and mobility patterns. These findings enhance our understanding of how resource optimization shaped human innovation and adaptation during periods of cultural and environmental fluctuation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Procrastination is a widespread problem with significant economic costs. In the U.S., delays in tasks such as tax filing result in millions of dollars in lost revenue. Reducing procrastination is crucial for lowering economic costs and improving well-being. Interventions such as breaking tasks into smaller subgoals and reducing the delay of reward are widely proposed in self-help books; however, their scientific foundation is lacking. Do these interventions truly work? For whom do they work most effectively? Why do they succeed or fail? This project applies cognitive science to answer these questions, providing a scientific foundation for designing more effective interventions tailored to individuals. This research investigates how two specific interventions affect procrastination, who benefits most from them, and why these interventions succeed or fail from the perspective of cognitive mechanisms. Specifically, it examines the effects of reward immediacy and subgoal-setting strategies, focusing on interim deadlines and subgoal size. The project adopts a dynamic decision-making perspective, viewing procrastination as an ongoing process influenced by shifting cognitive costs and motivation, rather than a one-time choice. Using a newly developed naturalistic experimental framework, this project moves beyond traditional lab-based tasks to systematically assess intervention effects in realistic task settings. Statistical models analyze how intervention effectiveness varies based on individual characteristics, such as perfectionism. To uncover the cognitive mechanisms underlying procrastination, computational models are developed and tested against behavioral data. This research has the potential to significantly advance our understanding of procrastination and lay the groundwork for more effective, personalized interventions that can improve productivity, reduce economic losses, and enhance overall well-being. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

In modern financial markets and economic systems with large populations, decision-making has evolved into a multifaceted process involving various aspects such as population heterogeneity, diverse information structures, and human-AI interactions. This project aims to develop new learning frameworks and mathematical foundations that strengthen our understanding of the stability, efficiency, and fairness of societal systems with large populations. Novel frameworks developed in this research are designed to have flexible model assumptions, be able to learn from incomplete information, and accommodate heterogeneous risk preferences as well as information asymmetry. This research will involve both undergraduate and graduate students, emphasizing cross-disciplinary training in mathematics and machine learning. This project places at its core the mathematical advancement of machine learning theory for stochastic systems with many interacting agents, known as “mean-field games”. The first goal is to develop new mathematical models and learning algorithms for mean-field games under structural properties such as graphon interactions or additional summary statistics of the population distribution. This development relies on new approximation schemes and stability analyses based on the local propagation of flows. The second goal focuses on principal-agent problems, where agents have diverse risk preferences or the capability to acquire new information. These topics pose significant challenges in a dynamic setting, leading to a novel class of stochastic partial differential equations that require new developments for well-definedness and regularity theory. The final goal focuses on constructing generative models (simulators) with interactive mean-field agents, addressing the scalability issue in agent-based simulator literature. To leverage the computational power of neural networks, a key objective is to establish a universal approximation theorem in the distributional sense and the convergence of an iterative deep-learning scheme to train the simulator. 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 · 2025-04

Project Summary: Discovery of glia-to-neuron identity conversion has opened the door for generation of new neurons to replace those lost to injury, aging or neurodegenerative disease including Huntington’s disease (HD) and Frontotemporal dementia (FTD). To this, I have used a therapeutically viable approach to successfully generate new neurons in the neurogenic niches of the aged adult mouse brain by transiently suppressing the RNA binding protein Polypyrimidine Tract Binding Protein-1 (PTBP1) using an antisense oligonucleotide (ASO) delivered by a single injection into cerebral spinal fluid. I further identified that Radial glial-like and subependymal-like cells (not astrocytes) convert into new neurons over a two-month period, acquire mature neuronal character, and functionally integrate into endogenous circuits that modify mouse behavior. Not yet established are the molecular events underlying glia-into-neuron identity conversion, including identification of the initiating glial cell(s), the events driving its conversion and subsequent maturation into a functional neuron, and application to HD and FTD. Additionally, the challenges pertaining to astrocyte-into-neuron conversion upon PTBP1 reduction have yet to be resolved. In this proposal, I will utilize combination of traditional single nuclear sequencing and immunofluorescence with a transformative single cell spatial transcriptomics technology, termed Multiplexed Error Robust Fluorescence In Situ Hybridization (MERFISH), to define the pathway(s) of generation of new neurons in aged neurogenic niches following a therapeutically viable injection to produce transient, ASO-mediated suppression of synthesis and accumulation of PTBP1. I will then extend the effort to test in mouse models of Frontotemporal dementia ( FTD) and Huntington’s disease (HD) whether glia-to- neuron conversion can generate functional replacement neurons. By leveraging my expertise with a network of collaborators I have assembled at UCSD and the surrounding research communities in San Diego, as well as strong collaboration with field leaders abroad, I will systematically identify the functionality, localization, cell origin and molecular pathways of glial cells undergoing identity conversion at multiple time points post conversion in healthy, FTD and HD contexts. During the mentored K99 phase, I will receive training under the guidance of Prof. Don Cleveland, who has trained more than 65 postdoctoral fellows, including 42 who at the end of their training obtained faculty positions. I have also assembled an outstanding team of collaborators including MERFISH pioneer Dr. Bogdan Bintu (UCSD), the Chief Scientific Officer of Ionis Pharmaceuticals Dr. C. Frank Bennett, neurogenesis expert Dr. Alysson Muotri (UCSD), and single nuclear sequencing expert Dr. Xin Jin (Scripps Research Institute) to assist my proposed research and provide me with additional scientific training and career support before, during, and after transitioning to an independent tenure-track faculty position. In the R00 phase, I will begin my long-term career goal of establishing a research program to understand adult neurogenesis and use mechanistic insights in reprograming for therapy development.

NIH Research Projects · FY 2026 · 2025-04

PROJECT SUMMARY. Nearly 4 million U.S. young adults aged 18-25 living with serious mental illnesses (YA-SMI) are at heightened risk for negative outcomes, particularly those who are low-income and experience complex trauma. Enhancing recovery for YA-SMI is, therefore, of great public health significance. In this study, recovery is defined as clinical recovery (i.e., symptoms), and personal recovery, which is a multidimensional process toward hope, meaning, and empowerment while managing symptoms. A key aspect of recovery is the development of self-management (SM) to reduce symptoms and enhance quality of life. Since young people spend nearly 40 hours a week listening to music, its potential as a SM strategy cannot be overlooked. Music is a widely used strategy for maintaining wellness because it is accessible, appealing, and culturally relevant. Yet, little is known about the mechanisms that may explain how music use influences recovery for YA-SMI. In response, NIMH partnered with the Sound Health initiative and the Trans-NIH Music and Health Working Group calling for mechanistic studies that address how music use can promote health and wellness. Informed by the Neurosequential Model of Therapeutics and Social Determinants of Mental Health (SDMH), this study uses an explanatory sequential mixed methods design to advance knowledge on how music use may influence recovery among YA-SMI. It will enhance our capacity to conceptualize and measure unique dimensions of personal recovery and apply experimental therapeutics (ET) to uncover how music use affects recovery. This study has three aims: 1) Identify key dimensions of personal recovery among YA-SMI by (a) evaluating the factor structure of the Recovery Assessment Scale (RAS) and (b) exploring in-depth the meaning of the identified factors to deepen our understanding of YA personal recovery; 2) Describe variation in healthy and unhealthy music use by key sociodemographic factors, SDMH, and recovery outcomes; 3) Apply ET to uncover (a) how music use influences hypothesized mediators and (b) the consequences of such ‘target engagement’ on recovery outcomes. Aim 1a uses secondary data from the sponsor’s NIMH study on YA-SMI (N=121) to factor analyze the RAS. A confirmatory factor analysis (CFA) will be conducted using structural equation modeling (SEM) to evaluate the five-factor structure among YA-SMI. Results will inform the collection and analysis of primary data with a Youth Advisory Board (N=6); we will recruit a purposive sample of YA-SMI (N=60 quantitative scales and subsample of N=25 qualitative interviews) in New York City. Aim 1b analyzes qualitative data on personal recovery using thematic analysis. Aim 2 analyzes mixed methods data on music use with thematic analysis, descriptive, and bivariate analyses. Aim 3 analyzes mixed methods data to uncover how music may affect recovery using grounded theory, descriptive, and bivariate analyses. Data will be integrated using the joint display method. This F31 involves substantive and methodological training and will identify promising targets that will initiate a program of research to co-create novel interventions for YA-SMI.

NSF Awards · FY 2025 · 2025-04

Languages have grammatical rules that define how words are used in different contexts, such as adding "-s" to a word to make it plural. People who speak more than one language must use different sets of rules depending on which language they are speaking. Multilingualism raises important questions about how the brain processes more than one language effectively. This project aims to study how this is accomplished by measuring brain responses while bilinguals produce words in different languages with different grammatical rules. Through these results, the project aims to provide insights into the neurobiology of language more generally and answer questions about how people learn multiple languages. The project also creates a large, publicly available bilingual brain database, provides opportunities for the public to learn more about how the brain processes language, and provides training for students in advanced neuroscience techniques. In detail, the project examines bilingual speech production through a series of experiments that measure brain responses using magnetoencephalography (MEG) while people say words that require them to produce a morphological change. By examining similarities in how morphological rules are used across multiple languages, the project aims to characterize when and where in the brain these processes take place. This approach can distinguish different theoretical models of bilingual language processing, specifically models that posit shared neural mechanisms across languages versus models that suggest distinct representations for each language. The project also examines how these processes are affected by the age of acquisition of a second language, potentially addressing important questions about second language learning. 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 · 2025-03

Non-technical abstract Superconductivity is a phenomenon in which a material’s electrical resistivity disappears. The remarkable property of the zero resistance has enabled many important applications, such as generating powerful magnetic fields for maglev trains and MRI machines. Moreover, superconductors hold significant promise for quantum computing, a technology that can drastically reduce the time required to solve specific computational problems. As such, advancing our understanding and control of superconductivity remains a cornerstone of condensed matter physics. Recent advances in laser technology have opened exciting possibilities for manipulating superconductivity on ultrafast timescales using light. However, many fundamental questions about superconductivity remain unanswered due to a lack of necessary tools capable of probing the underlying properties directly. This project seeks to address these challenges by developing and applying innovative ultrafast spectroscopic techniques to explore non-equilibrium superconductivity. In addition to its scientific goals, the project includes a robust educational component. It aims to enhance the curriculum in ultrafast and nonlinear optics, provide mentorship opportunities for graduate and undergraduate students, and conduct outreach activities with local schools in the New York area to inspire the next generation of scientists and engineers. Technical abstract This project investigates light-driven non-equilibrium dynamics in various superconductors by focusing on the terahertz (THz) frequency range, a natural energy scale of superconducting gap energy. Thus, THz driving can avoid injecting excess heat and lead to unprecedented phenomena, which provide unique properties of superconductivity that are unreachable by frequently used near-infrared excitation. Nevertheless, the study of non-equilibrium superconductivity remains in its infancy due to the lack of appropriate probes of superconductivity on the ultrafast time scale of a few picoseconds. The project will investigate THz-driven collective excitations of superconducting order parameters that can provide unique information, including couplings between two superconducting order parameters and pairing symmetry. This project employs newly developed THz multidimensional coherent spectroscopy for this goal. The project also aims to manipulate superconductivity optically, including its phase coherence and topology, using the recently proposed Floquet engineering. Raman spectroscopy is combined with THz driving to investigate the superconducting order parameter. The innovative spectroscopic techniques developed here are broadly applicable to diverse quantum materials, including frustrated magnets, topological insulators, and Moiré materials. The techniques establish a foundation for exploring light-driven non-equilibrium phenomena, potentially leading to groundbreaking discoveries in condensed matter physics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-03

PROJECT SUMMARY The K23 Career Development Award will strategically prepare the recipient to become a proficient scientist with a specialization in patient-oriented translational research study design and implementation, as well as an in- depth understanding of the neurobiology of oral cancer pain mechanisms. Additionally, the award will equip the recipient with the necessary professional skills to establish an independent career in patient-oriented orofacial pain research. BACKGROUND. Patients with oral cancer experience intense and incapacitating pain caused by mechanical pressure or stretching at the cancer site. The existing approaches for pain management, which involve opioid analgesics, have been inadequate. While the exact mechanisms that trigger pressure and stretch hypersensitivity in oral cancer patients are not yet fully understood, initial research indicates that patients express a functional pressure and stretch sensitive ion channel known as transient receptor potential vanilloid subtype 4 (TRPV4) in Schwann cells (SCs) that surround the axons of primary sensory neurons. In mouse models of oral cancer pain, TRPV4 inhibition in the oral cancer has been shown to reduce mechanical nociception in the orofacial region. SPECIFIC AIMS. This proposal aims to improve our understanding of oral cancer pain mechanisms by (1) developing and validating assays of pressure and stretch sensitivity, which will provide new methods to measure previously unexplored dimensions of oral cancer pain in patients; use genetically engineered mice to test the impact of (2) TRPV4 deletion in SCs on orofacial functional pain and (3) trigeminal neuronal hyperexcitability induced by pressure and stretch on the cancer. TRAINING. The candidate will be able to attain immediate objectives by leveraging a resource-rich institutional environment and a cohesive training plan that focuses on (1) clinical research protocol development and research methodology through a combination of didactic training and hands-on experience in patient-oriented translational pain research, and (2) utilization of genetically engineered mice to investigate the underlying mechanisms of oral cancer pain induced by pressure and stretch. MENTORSHIP. The candidate will receive support from two experienced mentors, Dr. Brian Schmidt and Dr. Donna Albertson. They have a long-standing expertise in oral cancer pain, cancer biology, and neurobiology. IMPACT. Improved understanding of the peripheral TRPV4 role in oral cancer pain offers significant potential for developing TRPV4 antagonists as novel analgesics for alleviating pain in oral cancer patients. The successful fulfillment of the proposed training program will equip the candidate with the essential abilities and expertise to lead an integrated research program on orofacial pain in an academic setting that involves both patient-oriented and laboratory research.

NSF Awards · FY 2025 · 2025-03

NON-TECHNICAL SUMMARY This CAREER award supports computational/theoretical research aimed at answering the question "How many materials exist?" Much of physics involves counting states (e.g., the number of possible configurations that a molecule or material can take) and predicting their probabilities. This idea is intuitive; mankind's earliest scientific curiosity about the natural world began with questions like "how many" stars, planets, elements, or substances exist. This project aims to develop methods for enumerating the possible outcomes of a physical process and calculating their probabilities. By leveraging these technical advances, the project will (i) elucidate the relationship between structural regularities in a material system and the number of available states; (ii) quantify the richness of compositional and structural diversity achievable by generative models for materials discovery-the complexity of the "materials landscape". Improving the ability to estimate the probability of states in diverse systems will be broadly impactful, extending beyond materials science to areas such as cellular reprogramming, ecology, control theory and machine learning. This project's scientific aims will be integrated into an extensive educational program designed to modernize the NYU undergraduate physics curriculum, aligning it with contemporary physics research and enhancing educational outcomes, supported by a systematic assessment plan. The program will also promote interdisciplinarity in education, enhance research education in molecular simulation, and develop outreach activities targeting K-12 education and public engagement. Additionally, interdisciplinary collaborations will extend the impact of this work to other domains of science and engineering. TECHNICAL SUMMARY This CAREER award supports computational and theoretical research aimed at efficiently sampling complex energy landscapes, predicting nonequilibrium entropies, and predicting the structure of novel inorganic crystals. Accurately computing the number and probabilities of states is fundamentally and practically important, whether we are interested in knowing the number and likelihood of different materials structures, the likelihood of generalizable solutions in neural network training, the stability of competing ecosystems, or the number of ways in which grains pack. When the states of interest are the stable structures of some dynamics, such as the energy minima found by gradient descent, we can compute the a priori probability of observing a state by measuring the volume of its basin of attraction. This project aims to advance basin volume calculations to elucidate the relationship between order and statistical regularities in material structures, and to quantify the diversity achievable by generative models for materials discovery. We will achieve this by introducing "Guided Monte Carlo" for basin sampling; systematically testing generative models on increasingly complex point cloud datasets generated via the FReSCo algorithm; and applying the basin volume method to generic dynamical systems (flow models and dynamical models of random close packing) for the first time. Beyond its scientific objectives, this project aims to leverage its research program to modernize the NYU undergraduate physics curriculum, promote interdisciplinarity in education, and advance research education in molecular simulation. The PI will integrate computational methods into the standard physics curriculum through a modular Computational Explorations in Physics class. Furthermore, the PI will introduce a Computational Science concentration, starting with a "Machine Learning for Science" course that explores the intersection of ML with Materials Science and other disciplines. This course will pilot an interdisciplinary team-teaching model that seeks to provide a more diverse educational experience and improve educational outcomes. Through the development of the NorSim Summer School, this project will address the shortage of training programs for doctoral and post-doctoral researchers in molecular simulation. Its core objectives are to prepare researchers for cutting-edge work, increase accessibility and diversity, and strengthen the community across disciplinary boundaries. Complementing these educational aims, the PI will implement outreach activities that combat social stereotypes and broaden the participation in physics among New York's varied urban population. This will be achieved through a public lecture series on contemporary physics in foreign languages and expanding existing efforts in K-12 outreach. Finally, the PI aims to extend the impact of this work to other domains of science and engineering via interdisciplinary collaborations. STATEMENT OF MERIT REVIEW 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 · 2025-03

Video is a uniquely powerful source of information about human behavior. Video provides data about what people do, documentation about how research is carried out, and provides demonstrations that inform scientists and the public about research results. Researchers across the sciences routinely use video as data, documentation, and demonstration. One such resource is 'Databrary,' the world’s only known large-scale repository specializing in storing and sharing research videos in behavioral sciences. Housed at New York University, Databrary was launched with NSF support. Databrary has removed the most significant barriers to video reuse while reinforcing core ethical principles of informed consent and restricted access to sensitive or identifiable data. Databrary now supports the research and teaching of thousands of scientists across the globe. This project updates and enhances Databrary’s software to make it an even more powerful and useful platform for research and teaching about human behavior. This updated version accelerates the reuse of shared data by making it easier to find target video clips or specific videos. The updates include tools for users to create their own custom collections for research or teaching. Upgraded software make it easier for scientists to organize their data before it is shared widely. Expert staff provide professional curation assistance to make shared data maximally useful to the widest possible audience. Researchers also create software libraries in R and Python that empower users to write their own code to access Databrary. Through substantial improvements to Databrary, the project enables novel, innovative, and data-intensive research about the characteristics and consequences of human behavior using powerful, flexible, affordable tools available in a web browser. The enhancements enrich datasets already shared on Databrary—many funded by taxpayers—thereby increasing the value of prior public investments in research. 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 · 2025-02

PROJECT SUMMARY/ABSTRACT Planning, the ability to mentally simulate possible futures toward a goal, is essential for everyday decision-making. Indeed, many psychiatric and neurological disorders are associated with dysfunctions in planning, including obsessive-compulsive disorder and autism spectrum disorder. However, the neural mechanisms underlying complex planning remain poorly understood, partly due to the disconnect between the oversimplified tasks used in most neuroscience studies and the rich, strategic planning required in real life. This project addresses this challenge by studying the neural basis of planning in "Four-in-a-Row," a game that captures the essence of complex planning while being amenable to detailed computational modeling. We posit that the brain plans by simulating promising sequences of future actions and evaluating their outcomes using a feature-based heuristic – a process we formalize in a computational model. To track this process in the brain, we will combine fMRI and MEG neuroimaging techniques and eye-tracking measurements, leveraging the computational model to guide the analyses of these data. Aim 1 will employ fMRI to identify brain regions representing the value of potential moves and the features involved in their evaluation. This spatial mapping will reveal where value and decision signals are computed in the brain during planning. Aim 2 will leverage the high temporal resolution of MEG to track the dynamic unfolding of mental simulation, allowing us to pinpoint when specific value computations and representations of possible future scenarios occur. Aim 3 expands our investigation by applying deep neural network models to extensive gameplay data, intending to uncover distinct, individualized planning strategies and improve our characterization of the neural mechanisms of complex planning. Overall, we will obtain a unified picture of planning, linking behavior, spatial neural correlates, and the temporal dynamics of this complex cognitive process. This research will be the first to provide direct neural evidence of mental simulation during planning, promising fundamental insights into a core aspect of human cognition. Our interdisciplinary team, with expertise in computational modeling, neuroimaging, and machine learning, is uniquely equipped to carry out this innovative research program. Although immediate translational applications are not the focus, our findings could eventually enhance the behavioral and neural characterization of planning deficits in various mental health disorders, potentially informing the design of more targeted interventions.

NSF Awards · FY 2025 · 2025-02

The world's infrastructure now critically depends upon cryptography for almost every task. While recent years have seen a blossoming of novel cryptographic techniques and applications, we still do not know if even our elementary cryptosystems are secure. Unlike most scientific domains, including much of computer science, it is impossible to empirically verify that a cryptosystem is secure. Instead, cryptographic security is founded on the conjectured existence of strong computational intractability: the inability of efficient algorithms to solve particular problems. The primary goal of this project is to strengthen the foundations of computational intractability needed for robust cryptography, which will yield a more robust cryptographic infrastructure. Another goal of this project is educational outreach, which will be presented through talks, tutorials, and lectures to encourage collaboration with diverse communities and, in turn, equip individuals with the ability to reason effectively about security. We propose exploring novel resource-constrained adversarial models to improve our cryptographic foundations. Traditionally, cryptographic attackers were modeled as probabilistic polynomial time algorithms. To circumvent barriers in this traditional setting and strengthen cryptographic security, we consider adversarial models that are alternately more powerful (e.g., algorithms with limited nondeterminism) and less powerful (e.g., algorithms with fixed polynomial time/space). We highlight two concrete aims: (1) improved cryptographic hardness amplification, yielding extremely hard problems from hard problems, and (2) understanding the feasibility of cryptographic security if it turns out that traditional cryptographic guarantees are impossible, planting our security on a firmer footing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

The real world is abundant with visual information, demanding both humans and intelligent systems to navigate and adapt to its complexity efficiently. Human visual processing can be broadly categorized into two types: intuitive processing, which enables rapid, experience-driven decision-making, and deliberate processing, which involves focused and systematic reasoning for complex tasks. Robust visual intelligence, the ability to integrate these types of processing, is fundamental to real-world problem solving and developing common-sense understanding. However, current artificial intelligence (AI) systems lack this level of robustness, as they often rely heavily on language models to compensate for deficiencies in visual architecture. This reliance becomes a critical bottleneck as tasks and scenarios grow more complex, limiting the adaptability and reliability of AI systems in practical applications. The project aims to build a hybrid, vision-centric framework that integrates intuitive and deliberate visual processing to create more robust visual intelligence. This improved capability has the potential to empower research in many other fields. For example, robotics researchers could use the system to help robots see, move and act more intelligently in challenging environments. Similarly, scientists and educators could use it to better understand complex diagrams, making it easier to uncover new insights and improve learning. The project aims to develop a hybrid approach that combines parametric (intuitive) and non-parametric (deliberate) visual processing methods to build robust visual intelligence. The first direction focuses on developing new ways to learn visual representations using techniques like visual self-supervised learning, language guidance, and generative modeling. And the goal for this direction is to advance vision-centric parametric knowledge to form the foundational layer of intuitive understanding. Building on this, the second direction incorporates human-like non-parametric mechanisms, such as visual search and working memory, to enhance deliberate reasoning capabilities. The third direction integrates these two approaches into a unified, hybrid architecture, where a high-level controller is capable of activating either method as needed for task-specific demands. Finally, the system will be tested in real-world, dynamic environments that extend beyond static image datasets, including tasks requiring long-form video analysis and visual-spatial reasoning. By applying the framework to these new challenges, the project aims to produce more adaptable, reliable, and broadly useful methods that expand the possibilities of vision-based applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

This research will investigate membrane protein evolution with the long-term goal of designing artificial cells with tailored functions. The specific objective of this project is to determine how mutations in membrane protein transporters impact dynamics, conformational equilibria, and the direction of transport, which will provide insight into the biogenesis of membrane proteins. This research will involve and train students and postdoctoral associates in biochemistry and biophysical chemistry. Professional training opportunities will be afforded to strengthen communication skills and to prepare trainees for future career paths. The project will also involve the development of a class for graduate students with the goal of teaching students advanced techniques in biochemistry and biophysical chemistry. The broader goal of this endeavor is to strike to a more equitable balance between lecture-based curricula and hands-on learning in graduate education. Many membrane protein transporters are comprised of a single polypeptide chain that contains inverted repeats within its structure. This research will investigate the underpinnings of membrane protein evolution by using mutations to derive a quantitative relationship between function and the free energy distinguishing conformational states required for function. The functional experiments will utilize a flow cytometry-based method capable of analyzing a library of mutations in a high throughput manner. To correlate the free energy with function, a quantitative method involving 19F NMR spectroscopy will be developed. Finally, theories about the evolution of ion-coupled transport mechanisms will be tested by using directed evolution experiments and bioinformatics. This project is supported by the Molecular Biophysics Cluster in the Division of Molecular and Cellular Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-02

Project Summary (R21) Neural progenitors produce an enormous diversity of neuronal and glial cell types to form a functional nervous system. With the advance of single-cell genomic technologies, more and more cell types with distinct transcriptomic signatures have been defined; yet there is a significant lack of genetic tools to identify, label, and manipulate specific cell types for developmental and functional studies. To efficiently and reliably identify useful GAL4 drivers expressed in a single cell type throughout development, we will take advantage of single- cell mRNA sequencing (scRNA-seq) and single nucleus RNA/ATACseq data to produce GAL4/split-GAL4 lines that provide targeted genetic access throughout development to any and all optic lobe neurons. Aim 1: Developing a collection of gene-specific split-GAL4 lines expressed in single cell types in the developing optic lobe. Our scMarco pipeline allows us to identify marker gene pairs in scRNA-seq data whose overlap selectively labels one specific optic lobe cell type. We will generate split-GAL4s inserted in these gene pairs that reproduce the expression of the genes. Their overlap will generate cell-type specific drivers. We have identified 123 genes to generate split-GAL4s hemi-drivers whose pair combinations can selectively target 81% of all optic lobe cell types. Aim 2: Using single-cell multiomics to identify enhancer fragments to generate cell-type-specific drivers. The expression pattern of most genes is controlled by multiple enhancers that each regulate the same gene in different cell types: These enhancers offer high cell-type-specificity. We will use our chromatin accessibility single-cell multiomics (RNA+ATAC-seq) datasets to identify enhancers individually specific to almost any neuron during development that will be cloned to generate GAL4 lines targeting these neuron types. Aim3: Combining gene-specific and enhancer-based split-GAL4 lines for complete neuron coverage. As not all cell types can be marked by a combination of gene specific split-GAL4 drivers or by specific enhancers, we will combine the two approaches above to produce tools targeting all cell types in the optic lobe. We will generate split-GAL4 lines with enhancer fragments that will be used in combination with the gene-specific- split-GAL4 lines. This will provide the cellular morphology and thus the identity of the neuron throughout development and will allow manipulation of their function of each neuron. Altogether, this collection of developmental cell-type-specific driver lines will be a valuable resource to study any cell type in the optic lobe, thus powering further developmental and functional studies. The gene- specific split-GAL4 tools (Aim 1) can also be used in any tissue of Drosophila where single cell transcriptomics is available, thereby expanding the power of this genetic approach to the whole fly community.

NIH Research Projects · FY 2026 · 2025-01

Sexual dimorphisms are common in nature. Although the processes that regulate them vary between taxa, a group of genes known as DM domain transcription factors (DMRTs) play a certain role in the development of male-specific traits. How these factors are linked to morphogenetic effectors, however, is unknown. Also, how the robustness of morphogenesis is achieved is also not well understood. Finally, it is unclear what parts of morphogenetic regulation, and its machinery are constrained versus plastic. As a model system to address these knowledge gaps, I use the four tail tip cells of Caenorhabditis elegans. During the last larval stage (L4), in males only, these cells undergo a morphogenetic process, Tail Tip Morphogenesis (TTM). The DMRT transcription factor (TF) DMD-3 is required and sufficient for TTM in C. elegans. Another DMRT TF; MAB-3, known to contribute to TTM robustness. Also, TTM has evolved repeatedly in nematodes, providing an opportunity to determine how conserved or evolvable TTM and its regulatory architecture is. My overall goal is to delineate what genes are transcriptionally controlled by DMD-3 and MAB-3 (directly and indirectly), to determine the extent of MAB-3's redundancy with DMD-3 (i.e. overlap in gene targets), and to determine what parts of this transcriptional control are constrained vs. plastic (via interspecific comparisons). To meet this goal, I will pursue the following specific aims: Aim 1 To delineate how DMD-3 transcriptionally controls TTM and how MAB-3 contributes to TTM robustness, I will compare transcriptomes of single tail tips throughout TTM from wild-type males vs. hermaphrodites (to identify male-specifically expressed genes) and dmd-3(-), mab-3(-), and dmd-3(-);mab-3(-) double mutants (to identify genes transcriptionally controlled by these TFs). By comparing my data to whole-worm DMD-3 ChIP-seq data previously obtained in the lab, I will parse which of these genes are likely direct vs. indirect targets of DMD-3 in TTM. Comparing all targets of DMD-3 to those of MAB-3, I will test how much of MAB-3’s contribution to robustness is due to overlapping vs. non-overlapping control. Aim 2 is to determine how plastic or constrained is the regulation of TTM across different nematode species by using the same tail-tip-specific RNA-seq approach on species in which TTM repeatedly evolved. This will identify which parts of the transcriptome have been conserved with respect to their expression profiles. For example, I will test the "hot-spot" hypothesis that DMD-3 (or a paralog) was repeatedly recruited when TTM evolved independently. This aim will help to identify which parts of this morphogenetic regulation are evolvable and which are conserved. The expected outcome is an understanding of how transcriptional regulation by DMD-3 and MAB-3 is linked to morphogenetic effectors, what these effectors are, how the redundant role of MAB-3 contributes to robustness, and what parts of the transcriptional regulation are conserved versus evolvable. Identifying key genes conserved in TTM is likely to contribute to understanding about general morphogenetic processes like wound healing, regeneration, and cancer metastasis.

NIH Research Projects · FY 2026 · 2025-01

The Vision Sciences Society is a nonprofit, membership organization of nearly 3000 scientists interested in functional aspects of vision, about 2000 of whom attend its annual May meeting. VSS was founded in 2001 to bring together scientists from a broad range of basic, translational and clinical disciplines, including visual psychophysics, visual neuroscience, computational vision and visual cognition. The scientific content of VSS meetings reflects the breadth of topics and interconnected ideas and approaches in modern vision science, from visual coding to perception, recognition and the visual control of action, recent developments in cognitive psychology, computer vision and neuroimaging. Since its founding, VSS has provided a forum for communicating advances in vision science, and has become a flagship conference for the field. The interdisciplinary nature of VSS is reflected in the membership of its Board of Directors and Abstract Review Committee. Many faculty from US institutions who attend VSS are principal investigators on NIH (mostly NEI) grants; the research objectives of the programs of NIH and NEI are well-represented in the program and individual presentations. Over 65% of participants are predoctoral and postdoctoral trainees. Of these, 55% are US citizens. VSS provides them career development opportunities via: (1) platform and poster presentations that serve as a forum for trainees to showcase their work and receive feedback, (2) career-development workshops that cover topics such as “Strategies for Funding your Research Ideas Around the Globe”, “Career Transitions”, “The Public Face of your Science”, “Careers in Industry and Government”, “Faculty Careers at Primarily Undergraduate Institutions”, and include panel discussions with journal editors, NIH and NSF program officers, as well as academic and industry representatives, and (3) a “Meet the Professors” event, where small groups of trainees meet with members of the VSS Board and other professors for free-wheeling, open-ended discussions. VSS provides formal and informal opportunities for networking with peers and senior colleagues in a comfortable, engaging setting. The large contingent of early-stage investigators at VSS is a sign of the strong health of the field and the opportunity VSS provides for advancing the field. Our goal is to facilitate access and participation for the next generation of vision scientists. VSS received a one-year R13 award for its 2019 meeting, followed by a 3-year renewal to support the 2022-2024 meetings. Based on the success and positive impact of these awards, the purpose of the current 5-year proposal is to provide 84 travel awards per year for trainees (undergraduates, graduate students and postdocs) to attend the 2025-2029 meetings. Our goal is to attract and support a wide, strong pool of trainees, who demonstrate potential for future success as vision researchers, and whose research findings are presented at the meeting. Funds are also requested to offset registration costs for all undergraduate attendees to significantly expand access and participation at the conference for students at their first encounter of the field of vision science.