New York University

NSF Awards · FY 2024 · 2024-09

This project harnesses the power of multi-dimensional tensor data to improve predictive accuracy and insights across crucial scientific and societal sectors through the development of advanced tensor classification techniques. Specifically, these techniques will facilitate early Alzheimer's diagnosis via sophisticated fMRI tensor analysis and improve the detection of anomalies in complex financial transactions. Despite the richness of tensor data, a significant barrier exists due to the scarcity of labeled instances, which are essential for effective statistical learning. These labels are often costly and labor-intensive to produce, particularly given the complex nature of tensor data. To address this challenge, the methods developed in the project will be optimized to perform robustly even with limited labeled data. By improving diagnostic tools and financial monitoring systems through enhanced tensor classification techniques, the project will support national health, economic security, and overall societal well-being. Moreover, it will promote interdisciplinary collaboration and educational growth, enhancing diversity in STEM fields and broadening participation across scientific and technological sectors. This initiative will not only drive scientific innovation but also serve national interests by improving public health, economic stability, and educating future scientists. This project will create computationally efficient and statistically optimal methods for tensor classification amidst the challenge of the scarcity of labeled data. The approach encompasses three innovative strategies: (i) employing low-rank discriminant tensors for high-dimensional tensor classification, (ii) utilizing abundant unlabeled tensor data for semi-supervised tensor learning, and (iii) adjusting for distributional differences between labeled and unlabeled data. The research team brings a strong theoretical foundation in tensor classification, supported by preliminary studies and experimental results. Collaborations with experts in biology, medical science, economics, computer science, and social science will facilitate the application of these new methods to a variety of pressing issues in these fields. This integrated approach is expected to yield significant advancements in tensor-based data analysis techniques, enhancing the capabilities and understanding across multiple disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Cervical cancer prevention is critical for cancer control. Many countries have introduced national cervical cancer prevention programs but coverage of key recommended prevention tools remains very low. Supporting guideline-based prevention strategies is an important way to improve cancer control. The Kupewa project (“prevent” in Chichewa) aims to (A) identify the optimal implementation strategies for increasing cervical cancer prevention for people who are living with HIV; (B) refine this set of optimized strategies by including information about the strategies’ implementability; and (C) ultimately identify the set of strategies that are effective, implementable, and show sustained effects. To our knowledge, this would be among the first applications of intervention optimization alongside implementation science. The project will leverage a robust research partnership between institutions and highly-qualified investigators. Implementation strategies for cancer prevention are understudied but urgently needed to accelerate cancer control globally.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY Visual performance (e.g., contrast sensitivity) varies as a function of eccentricity and polar angle: Performance worsens as eccentricity increases and is better along the horizontal than the vertical meridian (horizontal-vertical anisotropy, HVA) and along the lower- than upper-vertical meridian (vertical meridian asymmetry, VMA) at a fixed eccentricity. Contrast sensitivity and the extent of performance heterogeneity correlate with surface area in early occipital areas. A quantitative account relates the better performance at the fovea than peripheral locations to more cones and larger V1 cortical surface area, but does not suffice to explain polar angle asymmetries. A qualitative account posits that performance differences relate to differential system-level computations throughout the visual field, including efficiency –the ability to extract signal information, the amount of internal noise, and tuning –sensitivity to and selectivity for signal features. Here, we test the main hypothesis that performance differences throughout the visual field are attributed to both quantitatively varying surface area and qualitatively distinct representations and computations. We capitalize on the fact that presenting signals in noise enables quantifying internal noise and observers’ ability to extract and represent task-relevant information: We estimate efficiency and internal noise using the equivalent noise method (Aim1), and derive the perceptual representation of task-relevant features using psychophysical reverse correlation (Aim2&3). To link the quantitative and qualitative accounts, we examine whether the observed difference in performance and perceptual representation can be matched across locations by equating the V1 surface area encoding the stimuli for each individual observer (Aim3). Preliminary data suggest that distinct representations and computations underlie the performance heterogeneity throughout the visual field. Whereas the eccentricity effect may be due to lower internal noise at the fovea than perifovea (Aim1), both HVA and VMA may reflect a higher efficiency (Aim1) and better representation of task-relevant orientations (Aim2) at the horizontal than vertical meridian and at the lower- than upper-vertical meridian, whereas HVA and VMA may reflect differential SF-related computations (Aim2). We further evaluate whether differences in neural correlates go beyond the V1 surface area of each individual observer. We hypothesize that matching the V1 cortical area dedicated to processing the stimuli would eliminate the eccentricity effect, but at most reduce polar angle asymmetries (Aim3). By identifying the computations involved in basic visual tasks across eccentricity and polar angle, results will shed light on how the early visual cortex integrates neural resources to give rise to visual performance. Further, knowledge of the computations underlying varying performance across locations could have translational value for our understanding of visual deficits, and potential for developing human factors applications to optimally present information to observers in user interfaces. My long-term goal is to develop a model that takes into account computations and neural properties that can predict visual performance at various visual field locations.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Hox genes are crucial for patterning the early embryo along the anterior-posterior axis in all bilateral animal species. Hox dysregulation leads to severe developmental defects, including homeotic transformations. During early developmental time points and in pluripotent stem cells, Hox genes are repressed by the Polycomb group complexes (PcG). They are then activated by extracellular patterning signals like retinoid acid (RA) acting on RAREs (retinoic acid response elements), which activate the anterior Hox genes within the cluster. A CCCTC- binding factor (CTCF)-dependent boundary between Hox5 and Hox6 acts as an insulator, allowing for the RA signaling to activate only anterior genes maintaining PcG repression on posterior Hox genes. Recent developments in the field have characterized a sufficient boundary element to include CTCF and MAZ, a myc- associated zinc finger protein. Thus, this project aims to study activators and repressors further, which will be crucial for explaining the minimal elements sufficient to recapitulate epigenetic memory. By taking advantage of synthetic DNA technology developed in collaboration with the Boeke lab I can insert a highly editable synthetic Hox cluster (SynHoxA) into a pre-determined ectopic locus. This system allows for highly sensitive transcriptional and chromatin analyses of different SynHoxA variants. Using this system, we recently showed that the ectopic SynHox cluster itself contains all the information necessary to decode patterning signals. Thus, I propose to use the SynHoxA system to dissect the remaining two regulatory components required for a Hox cluster to respond to patterning signals. Aim 1 will tackle the relative contributions of RARE by testing whether the anterior Hox activation domain is a product of the additive RARE activity or whether each RARE activates specific Hox genes. I will measure the transcriptional output and chromatin modifications of SynHox variants carrying mutations of RARE to compare RARE activation of the anterior cluster. Aim 2 will identify PcG recruitment elements (PREs) and nucleation sites, which have not been identified in mammalian cells nor in the HoxA cluster. Although the ectopic SynHoxA cluster recruits PRC II, we find no evidence of it interacting with PRC II nucleation sites in trans. Therefore, I will generate overlapping constructs that can undergo a typical promoter bashing strategy to isolate “PRE-like” minimal elements. Finally, I will create a minimal construct containing all three elements required for epigenetic memory: activators (RARE), repressors (PcG), and boundary elements (CTCF+MAZ). This study will elucidate how Hox clusters receive patterning signal information and store it into stable epigenetic memory. This model will add to previous studies that have looked at the binding behavior of the activators without the context of chromatin boundaries and the establishment of transient signals and vice versa.

NSF Awards · FY 2024 · 2024-09

This award funds the research activities of Professor Matthew B. Kleban at New York University. Modern cosmology presents us with several profound mysteries. First is the nature of "dark energy,” invoked to account for the observational evidence showing that the expansion of the universe is accelerating. Second is dark matter, an equally mysterious substance or particle needed to account for the rotations rates of galaxies, along with many other lines of evidence. Last is the question of the universe’s origin and why it has so many seemingly fine-tuned features. A breakthrough in understanding any of these fundamental questions would promote the progress of science and advance the national interest by potentially revealing new laws of nature and helping answer profound questions about our universe. This award will fund Prof. Matthew Kleban’s research into theories of hypothetical particles called axions that could help us understand some of these features of the universe. Additionally, it will support his investigations of the physics of primordial black holes, tiny singularities cloaked by event horizons the size of a hydrogen atom, which could constitute dark matter. Lastly, it will enable him to continue his work on the quantum physics of accelerating universes, such as the one we appear to inhabit. His work will have a broad impact through public lectures and interviews, and in inspiring and training undergraduate and graduate physics students at NYU, as well as young post-doctoral researchers and early-career scientists. More technically, Prof. Kleban will study the multi-axion landscapes predicted by compactifications of type IIB string theory and their applications to cosmology. He will investigate potential origins for primordial black holes in the early universe, building on his recent work on that topic. He will refine a technique he pioneered recently to compute non-perturbative transition rates from time-dependent initial states. Finally, he will return to the topic of quantum de Sitter spacetime, investigating the extent to which recently developed tools can be applied to uncover a holographic dual 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 2024 · 2024-09

Productivity growth is a fundamental driver of increased living standards, and this project explores sources of manufacturing productivity growth as the United States rose to global economic prominence. The researchers seeks to understand firms’ technology adoption decisions during a past period of rapid technical change. There is a great deal of current policy discussion surrounding technology adoption, both in the United States and worldwide. The rapid pace of technological change naturally raises questions about how firms are able to adapt to changing environments. The historical experiences associated with the development of the American economy inform our understanding of the underlying sources of sustained long-run economic growth, which provides an historical benchmark for comparison and contrast with the modern era. The researchers’ preliminary estimates highlight the importance of ownership for firm behavior and outcomes. In milling, changes in ownership are associated with a substantial increase in the likelihood of firms upgrading from water power to steam power. Across all of manufacturing, female owners are more likely to own smaller establishments, but employ more women (and pay them more). The importance of ownership highlights how unequal access to funds and new technologies can be a driver of inequality. The researchers also explore how access to markets affected firm dynamics, as an expanding railroad network increased market integration. The main objective of this project is to compile a new open-access panel database of US manufacturing establishments from existing Census administrative records. The researchers have trained a team to digitize images of the handwritten Census manuscript pages and make panel links from one decade to the next. Through this project, the research team will complete these panel links, across all industries, and make the database easily accessible and freely available. The US Census of Manufactures is an establishment-level government census of all manufacturing establishments above a minimal size. The Census of Manufactures was professionalized and comprehensive beginning in 1850, and Census enumeration was done in-person by US Marshals. By making available new data on every manufacturing establishment, linked over time, this project enables further research into the historical development of the American economy. This database allows researchers and policymakers to understand better the underlying drivers of economic growth and improvements in living standards. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Safety is a crucial requirement for systems employing reinforcement learning in domains such as robotics, autonomous driving, and power systems. In this project we consider safety as the avoidance of known unsafe states and prevention of unknown unsafe behaviors. To achieve this safety goal, we propose a suite of model-based reinforcement learning approaches that span training, deployment, improvement, and evaluation. The project consists of the following research thrusts: 1) Training policies that are robust to distribution shift via distributionally robust approaches; 2) Continual policy improvement via Bayesian risk-averse learning; 3) Adapting policies to non-stationarity via online change detection; and 4) Rigorous simulation via space-filling experiment design to gain understandings of a given policy in various environment settings. If successful, the proposed research will make significant contributions to the existing literature on safe reinforcement learning (RL) by developing new theories and methodologies. In particular, the proposed research has the following innovations: 1) formulation of safety measures as general objectives beyond the standard cumulative form and development of solution approaches for this general formulation; 2) consideration of both intrinsic uncertainty and model uncertainty to ensure that the resulting policy performs well and satisfies a specified risk level in the real environment; 3) bridging the gap between Bayesian RL and safe RL for continually improving models and policies while maintaining the safety of the deployed policy; 4) near-optimal policy learning algorithms that adapt to piecewise non-stationary environments; and 5) rigorous simulation approach for policy evaluation to identify unexpected unsafe behaviors before they actually happen. Because of the generality of the proposed approaches, the resulting techniques will have broad applicability in various domains that utilize reinforcement learning and require safety considerations. This research integrates well with the courses that the PIs have developed and teach. The PIs are committed to promoting broad participation within their research communities by actively engaging students in research and mentoring for academia careers, outreaching to K-12 students, and fostering greater participation of a wide variety of researchers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Many of the sensations we perceive are caused by our own actions, which we can distinguish from externally generated stimuli. In the auditory system, the ability to differentiate between external and self-generated sounds is crucial for functions such as vocal communication, musical training and general auditory perception. The tight correlation between motor-related signals, or corollary discharge, and the timing of incoming sensory information is leveraged by the auditory system to discern that a given sound is self-generated. Throughout our lifespan, we learn that certain movements predictably elicit reproducible sounds. However, in different contextual settings, the same movements can yield novel sounds that violate expectations from our previous experiences, and we must update our predictions about the sensory consequences of our actions. Neural responses in sensory regions of the brain are sensitive to expectations, such that expected self-generated sounds are suppressed in the primary auditory cortex (A1) and unexpected sounds elicit responses from “prediction error” neurons. The predictive suppression of expected self-generated sounds in A1 is mediated by secondary motor cortex (M2) inputs to A1 neurons, which serve as a potential source for establishing specific associations between sounds and their corresponding movements. However, the function of prediction error signals and the mechanisms underlying their utilization in generating neuronal representations for newly encountered self-generated sounds remains unclear. The primary objective of this project is to integrate quantitative behavior, cellular imaging, and circuit perturbations to examine how coordinated activity between the motor and auditory cortices encodes movements with various acoustic outcomes and tests the hypothesis that corollary discharge signals do not simply encode action, but instead convey rich information to sensory cortex about movements and their expected acoustic consequences. Specifically, we will utilize chronic two-photon (2P) calcium imaging to examine the response patterns of neuronal ensembles in M2 as mice acquire the association between a lever-pressing behavior and an accompanying sound. Through changing the sound associated with the lever-press movement, we will assess the plasticity and reorganization of these circuits as mice learn a new self-generated sound (Aim 1). To further explore the role of the motor cortex in encoding movement with its sensory consequences, we will employ a chemogenetic approach to selectively inhibit M2 activity at various stages of learning new acoustic associations and evaluate whether novel sounds can eventually be suppressed in the auditory cortex (Aim 2). Lastly, we aim to determine whether M2 selectively communicates sound-related corollary discharge signals to A1 relative to other sensory cortices. (Aim 3). Overall, these experiments will provide valuable insights into the brain’s mechanisms for predicting and updating the acoustic consequences of our actions in real time and could uncover fundamental principles underlying the dynamic information flow between sensory and motor regions of the brain.

NSF Awards · FY 2024 · 2024-09

The highest energy particles in the Universe are Ultrahigh Energy Cosmic Rays (UHECRs), whose energies are more than 100,000 times higher than the highest energy particles which humans have been able to produce in our ground-based accelerators. How Nature can produce such high energy particles has long been a major puzzle in astroparticle physics. The PI has recently proposed that UHECRs are produced in binary neutron star (BNS) mergers, which can explain all the numerous constraints on their sources and has not yet been ruled out. With this award, the PI and her graduate students and collaborators will test the BNS merger hypothesis. They will develop tools to exploit information on individual UHECR composition coming from the upgraded Pierre Auger Observatory. The team has a wide-ranging broader impacts program including public lectures, mentoring of junior researchers at all levels, and investigating how to expand outreach to include older adults. UHECRs are charged particles and thus deflected in extragalactic magnetic fields, delaying their arrival relative to neutrinos, gamma rays and gravitational waves produced in the same event by hundreds of thousands of years. As well as discovering the origin of these highest energy particle messengers (UHECRS, Very High Energy neutrinos, and gamma rays), this project will characterize in detail the Galactic magnetic field and understand its origin, and make progress identifying Dark Matter and constraining its interactions with ordinary matter. This will help to backtrack UHECR arrival directions and anisotropies, trusting that the nearest sources may stand out sufficiently over the ensemble contribution to be identifiable. As it is difficult to infer the properties of individual UHECR events, the study includes applying generative AI techniques to tune model parameters more accurately, working with expert collaborators at the Simons Center for Computational Astrophysics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Cities are loci of resource consumption, economic activity, and innovation. Given the increasing ability to collect, transmit, store, and analyze data, there is the opportunity to go beyond today’s understanding of cities to enable better operations, better planning, and better policies. While there are already troves of open data about cities, their potential remains underexplored because of unique challenges related to the diversity and scale of urban data and the complex computations required to obtain trustworthy insights. This project builds tools and infrastructure that meet the unique requirements of urban computing. The open-source cyberinfrastructure supports data-driven exploration and empowers a broad range of stakeholders to analyze and model urban data at scale. This cyberinfrastructure serves as a catalyst to create and sustain a cohesive community around urban computing. By enabling sharing and collaboration, this cyberinfrastructure also streamlines and advances urban research and democratizes urban computing. The project includes activities and mechanisms to engage the community and integrate the results to support education. This project addresses two critical obstacles in urban computing: (1) the lack of documented, robust, well-engineered tools and open computing platforms and (2) the dispersed community of cross-disciplinary researchers and developers, which limits knowledge sharing and collective solutions. A core component of the project is the development of a cyberinfrastructure that integrates methods and tools for the exploration of urban data that are scalable, reusable, and interoperable, and solutions to common challenges, including data discovery, cleaning, analytics, modeling, visualization, and reproducibility. The project deploys a cloud-based, open, collaborative environment that supports the use of this infrastructure over large and diverse urban data sets, allowing communities of users to quickly create analyses that are reproducible by design and that can be debugged, shared, and extended. The intellectual merit lies within the novelty of the tools and techniques it produces, as well as in the software engineering challenges involved in developing, maintaining, and supporting cyberinfrastructure that will be deployed and widely adopted. This Office of Advanced Cyberinfrastrucure project is jointly funded by the Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program and the National Discovery Cloud for Climate (NDC-C). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Many important questions in the natural sciences and in engineering involve nonlinear phenomena, mathematically described by nonlinear equations. Solving these problems typically requires iterative algorithms like Newton's method, which linearizes the nonlinear problem in each iteration. Newton's method is known for its rapid local convergence. However, the convergence theory only applies when the initialization is (very) close to the unknown solution. Thus, relying on local convergence theory is often impractical. Farther from the solution, small Newton updates are typically necessary to prevent divergence, leading to slow overall convergence. This project aims to develop better nonlinear solvers. This will benefit outer-loop problems, such as parameter estimation, learning, control, or design problems, which typically require solving many nonlinear (inner) problems. The project will also support the training and research of at least one graduate student, the mentoring of undergraduate students through the Courant’s Summer Undergraduate Research Experience (SURE) program, and the outreach to K-12 students through the cSplash activity in New York City. To address issues of slow nonlinear convergence, This project aims to develop methods that lift the nonlinear system to a higher-dimensional space, enabling the application of nonlinear transformations that can mitigate nonlinearity before Newton linearization. The project will develop and systematically study the resulting novel Newton methods for severely nonlinear systems of partial differential equations (PDEs). The proposed lifting and transformation method can be interpreted as nonlinear preconditioning, a research area much less developed than preconditioning for linear systems. The goal of this project is to study for which classes of nonlinear PDE problems this approach improves convergence, to theoretically analyze why, and to make these methods a more broadly accessible tool for solving severely nonlinear systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Effective urban planning and transportation system designs require comprehensive urban mobility data, such as pedestrian statistics. However, existing data collection methods, which rely on manual counting or AI-based video analysis from fixed traffic cameras, often overlook underserved communities such as older adults who need safer and more accessible public spaces to navigate their day-to-day activities. This project aims to address these data unfairness and scarcity issues by developing and deploying low-cost, battery-powered smart cameras. These devices will enhance the fairness and scalability of data acquisition, particularly benefiting older adults, people with disabilities, and other underserved communities in accessing mobility services. By ensuring privacy-preserving data acquisition, the project aligns with the NSF's mission to promote the progress of science and advance national health, prosperity, and welfare. This initiative will contribute to more equitable urban planning and foster significant advances in smart city technologies. The proposed project introduces an innovative effort to democratize urban mobility data acquisition by developing and deploying low-cost, battery-powered smart cameras. The research will focus on creating new control and perception algorithms to operate these devices efficiently within the constraints of battery life and computational capacity. Specifically, the project will develop (1) on-device adaptive sampling techniques to optimize power consumption by enabling smart cameras to perceive and react to real-time environmental changes, and (2) methods for uncertainty quantification in AI-based video analysis outputs to support robust and resilient decision-making in dynamic urban settings. The project includes prototyping and pilot field experiments in underserved senior communities in NYC. Collaboration with Geriatrics researchers will explore the social aspects of this technology, enhancing our understanding of how underserved communities perceive and accept AI technologies in transportation services. This interdisciplinary research will bridge AI, transportation, computer vision, and Geriatrics, promoting data fairness and energy-efficient AI. The project's open-source hardware design, algorithms, and privacy-preserving data will facilitate broader applications and contribute to senior safety and healthy aging. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This EArly-concept Grant for Exploratory Research (EAGER) grant will leverage cutting-edge technology, including virtual reality and wearable devices, to study how individuals with visual impairment navigate through crowds. Most individuals with visual impairment live in urban areas where job opportunities and healthcare services are more accessible. Navigating dynamic city environments, particularly crowded spaces such as subway stations and bustling streets, is challenging for individuals with visual impairment who rely on auditory and tactile cues for navigation. These challenges are exacerbated in emergency situations by the need for swift and accurate responses that become essential for physical safety. By creating simulated urban environments enriched with advanced auditory and haptic cues in virtual reality, the project aims to elucidate sensorimotor interactions and cognitive processes underpinning the navigation of persons with visual impairment through crowded spaces. The insights gained from this research will inform the development of wearable technologies that enhance the safety and independence of individuals with visual impairment, thereby promoting their health, prosperity, and welfare. This research project fills crucial knowledge gaps in understanding how persons with visual impairment navigate dynamic urban environments. By studying their cognition and behavior in immersive virtual reality settings, the project will provide insights for developing better assistive technologies and enterprise resilience strategies. Lived experiences of patients with glaucoma will inform the design of the multisensory tasks to reflect real-world challenges faced by persons with impairment. In these realistic virtual reality environments, participants will interact with synthetic actors who form a virtual crowd, and experience various urban scenarios enriched with visual, tactile, and auditory cues. The project will emphasize physics-based integration of these cues within the virtual reality environment to create immersive experiences mirroring real-world challenges. All three sensory cues will play a vital role in guiding navigation decisions and interactions through virtual crowds. Data collected in experiments will be explored within a novel network-inference approach to identify the cognitive and behavioral processes each person undergoes to make effective and efficient navigation decisions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This award will fund research that attempts to establish a new framework for digital twin modeling of urban energy delivery strategies. This framework is grounded in logistics and platform economics and leverages both model-based and data-driven approaches, along with a living lab and industry collaboration. The research project aims to address fundamental challenges in integrating electric mobility into urban infrastructure, ensuring efficient energy delivery while promoting sustainability and economic viability. The project's significance lies in its potential to enhance urban energy efficiency, reduce greenhouse gas emissions, and support the integration of renewable energy sources in a manner that inclusively serves all communities. By fostering collaboration between academia and industry, this research will contribute to the advancement of science and technology in urban energy systems. Additionally, it will support education by providing students with hands-on experience and exposure to interdisciplinary topics in energy and mobility with real-world data. The project also aims to promote diversity in STEM fields by engaging a broad range of students and researchers. The research consists of three thrusts designed to address the complexities of urban energy delivery through digital twin modeling. The first thrust will design a three-sided market modeling framework that captures the interactions between travelers, mobility providers, and energy providers. This framework will incorporate mechanisms from logistics and platform economics to understand and optimize the dynamics within electric mobility markets. The second thrust focuses on developing a scalable digital twin environment to support comprehensive analysis and decision-making for these ecosystems. This environment will integrate both model-based and data-driven approaches, enabling the simulation and evaluation of various strategies and their impacts on urban energy delivery. The third thrust aims to create an "energy delivery playbook" by identifying diverse strategies through industry collaboration and testing them in theoretical and living lab environments. This playbook will provide guidelines and best practices for implementing effective energy delivery solutions in urban settings. The research will establish fundamental bounds on the achievable performance of three-sided electric mobility markets and develop tools and algorithms with guaranteed performance metrics. By combining theoretical analysis with practical application, the project will offer robust solutions to enhance the efficiency and sustainability of urban energy systems. The integration of this research into education through a dedicated software platform will further extend its impact, offering students valuable experience and fostering the next generation of infrastructure experts. 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 2024 · 2024-09

PROJECT SUMMARY/ABSTRACT Pharmacologic therapy for common forms of chronic pain is ineffective and plagued with side effects. Our long- term goal is to reveal mechanisms of pain/nociceptive signaling and define drug targets. G protein-coupled receptors (GPCRs) control most patho-physiological processes, including pain, and are the target of 34% of therapeutic drugs. GPCRs are considered to function solely at the plasma membrane, where they interact with extracellular ligands and couple to intracellular G proteins. However, agonists released from injured and diseased tissues evoke redistribution of GPCRs to endosomes in neurons. These endosomal GPCRs (eGPCRs) generate sustained signals in subcellular compartments that control the ion channel activity that underlies chronic pain. The central hypothesis is that activation of pronociceptive eGPCRs produces nociceptive signaling and most forms of chronic pain; antagonists of eGPCRs block nociceptive signaling and are anti-nociceptive. The rationale for this proposal is that discovery of eGPCR pain mechanisms will facilitate development of drugs that selectively antagonize eGPCRs in neurons and provide superior pain relief with fewer side effects. The overall objectives are to discover mechanisms underlying chronic pain and validate a therapeutic target. The central hypothesis will be tested by pursuing three specific aims: 1) Discover the mechanisms of eGPCR signaling in subcellular compartments of neurons; biophysical and imaging approaches will be used; nanoparticles (NPs) will be designed with components that target neurons, promote endocytosis and release eGPCR ligands in the acidic endosome; 2) Discover the mechanisms by which eGPCRs regulate ion channels that control neuron activity; ion channel activity and excitability of neurons will be studied with electrophysiology. NP-encapsulated drug probes will define the role of eGPCRs in neuronal excitation; 3) Validate eGPCRs as a therapeutic target for chronic inflammatory, neuropathic and cancer pain; NP-encapsulated eGPCR ligands will be compared to conventional therapy in three pain models. The proposed pain mechanism is a novel explanation that resolves the enigma of widespread clinical trial failures of GPCR-targeted drugs. Innovation in the proposal extends to the NP approach to probe the mechanism and validate the target. The proposal is clinically significant because it validates an eGPCR-target that offers superior pain relief with fewer side-effects and is applicable to most patients with intractable chronic pain.

NSF Awards · FY 2024 · 2024-09

This project will broaden participation in engineering by developing learning resources through which Black families have opportunities to engage in engineering practices and to see themselves as part of the engineering community. The research team will co-develop informal learning resources with Black families in which children, ages six to ten, have opportunities to engage in biological, civil, computer, electrical, environmental, and mechanical engineering activities at home. Caregivers will support their children through engineering practices such as empathizing, defining, ideating, prototyping, and testing, while also educating them about Black engineers and scientists who made significant advancements within each field. Research will explore whether and how the identity-affirming informal learning resources fostered the children’s engineering identities and interest. The resulting deliverables include video workshops for caregivers, to support them in using the resources, as well as a suite of easy-to-use engineering activities that will be disseminated via national homeschool networks, through public media, through high-traffic repositories with engineering lesson plans, and through professional networks of science and engineering educators. Research will explore how identity-affirming engineering educational resources impact children’s engineering identities and interests. To investigate whether and how these resources contribute to shifts in children’s engineering identities and interests, the research team will conduct a mixed-method study in which they generate and analyze the following data sources: pre- and post-engagement surveys with the caregivers; video-recordings of caregiver-child interactions as they engage with the informal learning resources; interviews with children and caregivers; caregiver reflective journals; and artifacts produced by the families, such as children’s sketches. The results from these analyses will provide insights into how informal educators can design at-home learning resources that build children’s interests in engineering pathways, as well as how families can use identity-affirming interactions in engineering to spark their children’s interest in this field. Findings will be disseminated widely via professional conferences, networks, and journals in educational research. Ultimately, this project is likely to broaden participation in engineering among Black people who remain underrepresented in engineering pathways and careers. This Integrating Research and Practice project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of STEM learning in informal environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The underlying geophysical connections linking great earthquakes with plate tectonics will be addressed using sophisticated models. These seismic events are the largest earthquakes with magnitudes of nine or higher and are among largest sources of natural hazard affecting many countries, including the United States. Scientists have long known that these earthquakes are linked to plate tectonics and occur where an oceanic plate dives into the earth’s interior at a subduction zone, like the one along the coasts of Washington State, Oregon and northern California. Unfortunately, fundamental issues remain as to the reasons where and when they occur. This interdisciplinary team will link the long-term physics driving and resisting plate tectonics with that of great earthquakes. The outcome of the models will be a deeper understanding of earthquake occurrence. Currently, the ability to solve the set of equations describing the physics of this coupled problem is beyond the ability of mathematical methods. Consequently, this project brings together mathematical scientists with earth scientists to attempt to solve this problem collaboratively. Mathematically, problems like this are solved on the largest supercomputers and are described by many equations. For the plate tectonics problem by itself, the equations change only a small amount from one moment of time to the next in the computer model, but in this coupled problem only a set of the equations change when an earthquake happens and so the mathematicians will discover and implement new ways to solve such large sets of equations. Meanwhile, the earth scientists will use the new methods to understand the basic physics of the coupled earthquake and plate tectonics problem, ultimately tailoring the methods to models of individual plates and faults, such as the Juan de Fuca Plate which subducts below the Pacific northwest. The algorithms are expected to efficiently use the largest supercomputers now in the planning stage, including the NSF-planned LCCF (Leadership-Class Computing Facility). Moreover, the computer software, called Rhea, will be distributed open-source and will be well-engineered and documented. The team will collaborate with the Computational Infrastructure for Geodynamics, supported by the NSF, for the distribution of Rhea to the broader scientific community. The PIs will train graduate students at Caltech, Virginia Tech, and NYU at the boundary between the mathematical sciences and science applications. The team will participate in outreach programs: In California through a program that brings geophysical science to local Title I schools; in Virginia, through one that provides outreach projects for local high schools; and in New York City, through a program that focuses on exposing undergraduates to mathematical research. The forces controlling plate tectonics and the conditions leading to great earthquakes are currently treated as separate, fundamental problems, but in this project, they will be linked with a focused effort to develop and apply a new generation of finite element methods with solver adaptivity that will scale on the largest computers. The activity will involve major advances in mathematical and computational algorithms for multi-physics problems, the team will bridge the space–time divide and self-consistently compute the long-term motions of tectonic plates and the intervening space–time evolution of stress within and adjacent to plate boundaries. This undertaking is beyond currently available methods and mathematically requires new concepts to allow tracking the shifting—but localized—regions of enormous computational need during earthquakes. The team will expand the notion of space and time discretization adaptivity towards solver adaptivity. Solver adaptivity will use equation residuals to focus computing resources towards the most efficient solution of large linear and nonlinear systems of equations. Since the system arising by discretizing the equations in the earthquake–plate tectonic problem typically has tens and hundreds of millions of unknowns, solvers based on matrix factorization are out of question and one must rely on iterative solvers that also allow parallelization. The algorithms are expected to scale on the largest anticipated supercomputers with distributed memory and computational elements, such as the NSF-planned LCCF (Leadership-Class Computing Facility). As such, the scalable algorithms will fill an important need and demonstrate the efficient use of future LCCF machines. The methods will be incorporated into the highly scalable Stokes solver, finite element package Rhea. Visco-elasticity and frictional material models will be implemented into Rhea. The science and mathematical challenges will be addressed with an interdisciplinary team consisting of a geophysicist who works on the dynamics of plate tectonics, a mechanician who works on the physics of earthquakes, and applied mathematicians who work on linear and nonlinear scalable PDE solvers. The team will apply the methods to understand the coupled physics generically, first in two dimensions and then in three dimensions. Then, using models regionally tailored by the explicit incorporation of seismic, geologic and fault structure, they will simulate Cascadia and the northwestern Pacific subduction systems. This project is jointly supported by the Computational and Data-Enabled Science and Engineering in mathematical and Statistical Sciences program in the Division of Mathematical Sciences and the Geophysics program in the Division of Earth Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Crystallizations are resource-intensive purification techniques in the manufacture of materials and chemicals that society uses every day. Nucleation is the birth of a crystal from a solution, in which molecules come together to form the tiny nucleus that eventually grows into a larger crystal. This nucleation process is critical to produce valuable products, such as dyes and pharmaceuticals. However, it requires significant amounts of chemical solvents and energy. This project seeks to improve the sustainability of crystallizations by pinpointing mechanisms by which light can induce nucleation. Using an external trigger, such as light, to induce and control nucleation has the potential to reduce the environmental impacts of crystallizations in industrial processes. The project will also develop basic design rules that may ultimately provide better control over crystal shape and the arrangement of molecules to properties that can be optimized for the particular application of the materials. The investigators will also create educational activities that train undergraduate and graduate students from diverse backgrounds to design “greener” crystallizations, making it an inherent part of basic chemical engineering education. By collaborating with the Applied Research Innovations in Science and Engineering (ARISE) program, the investigators will mentor underrepresented high-school students who will complete summer research experiences. The overall mission of this research program will be to design computer-aided, high-throughput crystallization experiments to quantify the mechanisms that govern non-photochemical laser induced nucleation (NPLIN). Of specific interest will be the quantification of the conditions in which the dielectric polarization (DP) and colloidal impurity (CI) mechanisms govern light-induced nucleation. The novel computer-aided experimental methodology will examine three elementary crystallizations: i) urea as a model single-step nucleation, ii) glycine as a two-step nucleation, and iii) amino acid oligomers, such as glycylglycine and triglycine, that are building blocks for proteins. Continuous-flow, high-pressure microfluidic devices coupled to a laser will be designed and implemented to switch off the CI mechanism by suppressing the formation of nanobubbles. Supervised machine learning methods will be trained with data collected at ambient and elevated pressures to build design rules for the DP and CI mechanisms. Computer-aided experimental methods for the study of crystal nucleation mechanisms, a field that remains vastly an art and based on outdated batch techniques, is an emerging area of science that has the potential to create new unit operations in industrial processes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This project seeks to harness the power of open-source development for the creation of new technology solutions to problems of national and societal importance. Across the globe, software is integral to many aspects of contemporary life, making it crucial to be able to accurately identify and verify the components from which software is created. A well-known method for creating an inventory of software components, known as a Software Bill of Materials (SBOM), is prone to inaccuracies and is vulnerable to unauthorized tampering. This project, called “SBOMit,” aims to transform SBOMs by creating an open-source ecosystem that redefines how SBOMs are produced and checked. The SBOMit open-source ecosystem aligns with both the NSF's mission to enhance national welfare and security as well as the objectives of the National Cybersecurity Strategy. SBOMit minimizes the risks of tampering with SBOMs and improves transparency, establishing a new benchmark for trust in software supply chains. As an open-source product, SBOMit is available to industry, government, and other sectors at no cost, providing a free and open solution to one of the most important cybersecurity problems. SBOMit's innovation builds upon the capabilities of another open-source product, known as “in-toto,” to bolster SBOM reliability against malicious threats, leveraging the in-toto framework’s attestations to enhance SBOM trustworthiness. The in-toto framework ensures cryptographic verification at every stage of the supply chain, providing assurance of software integrity for version control systems, the continuous integration/continuous deployment process, testing, and all other stages of the software supply chain. Cryptographic verification serves dual purposes: diminishing SBOM tampering risks and setting a new transparency and trust standard in software supply chains. Additionally, the capability to leverage in-toto's attestations helps to fortify software against a variety of existing and anticipated software supply chain attacks. Industry participants within the growing SBOMit ecosystem are developing tools that will help to integrate the product across a diverse range of applications, fostering a community-driven approach that will protect software used in industry, government, academia, and elsewhere. The detailed metadata embedded within SBOMs by in-toto attestations facilitates a greater level of visibility and control of the provenance of components, empowering automated verification processes that can detect and defend against unauthorized modifications. As the SBOMit open-source ecosystem expands by integrating the product across various software domains, this standardizes a means to generate SBOMs and empower stakeholders across sectors with transparent, secure, and easy-to-use tools. The SBOMit open-source ecosystem can pave the way for the trustworthy software supply chain of the future. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

In this R25 application, NYU Steinhardt faculty in rehabilitation health professions research (RHP, including physical therapy, occupational therapy, communicative sciences and disorders, music therapy, drama therapy and art therapy) and education research will partner with College and Career Lab (CCL) and create a research pathway, NYU CCL - IncreasinG RepresentatioN In RehabilItation and CreaTivE Health Professions Research, “CCL IGNITE”. Guided by findings from contemporary educational research and substantial preliminary work, CCL-IGNITE will deploy a robust exposure and engagement-driven curriculum, designed jointly by educators and researchers at CCL and NYU Steinhardt. The central premise of CCL-IGNITE that early access, increased resources and sustained engagement will improve educational outcomes. In this novel pathway, for the first time, we implement key strategies that are known to mitigate key barriers to representation in other fields (such as medicine and nursing) in RHP research. We will increase access by providing early and sustained exposure to RHP careers and research, and by generating novel NGSS-aligned culturally relevant RHP science education. We will and offer increased resources supporting academic and career preparation and offering financial support, and will increase engagement by deploying a robust mentorship model and student-centered teaching. We include a rigorous impact evaluation that overcomes the pitfalls of conventional program assessment. Key outcomes of CCL-IGNITE include: 1) Development of a RHP research pathway for 450 students (150 high school and 300 middle school students), 2) Novel RHP research-related science education resources will be developed during the course of this project and disseminated through CCL-IGNITE website, immersive techniology (augmented reality / virtual reality), 3) Professional development open houses for teacher engagement (n=150 teachers), 4) Community labs to disseminate CCL-IGNITE materials to students, teachers, parents (n=1000), 5) Dissemination of findings in the peer-reviewed literature through conference presentations and manuscripts to facilitate translation to practice, 6) Qualitative and quantitative data will also be used for five iterations of program refinement (one for each year of grant award) This application has high potential for substantial positive impact as CCL-IGNITE will leverage university-community partnerships in an urban area and encourage youth to pursue further studies and careers in RHP research, consistent with the mission of NIGMS. Additionally, this application will generate novel NGSS-aligned science education resources related to rehabilitation and creative arts-based therapies research, and our findings will advance the field of evidence-based education practice.

NIH Research Projects · FY 2026 · 2024-08

ABSTRACT In the United States, Black and Hispanic women (BHW) have the greatest risks of poverty, type 2 diabetes (T2D), and depression, problems which represent overlapping and mutually reinforcing epidemics. In the context of HIV, the economic, metabolic, and mental health risks are even greater, but they remain inadequately examined. Although depression disproportionately affects women with T2D and HIV, it is often missed when the three co-occur. Increased potential for missed care is of particular concern for BHW, who experience disproportionate comorbidity, socioeconomic risk, and mental health misdiagnosis. Elucidating the biology underlying relationships between poverty, depression, and metabolic health is a critical step toward improving clinical recognition of depression and identifying potent macro- and molecular-level targets for future research aimed at eliminating health disparities. Diagnosis-agnostic approaches suggest that depressive subtypes are differentially linked to social risk factors, metabolic indices, epigenetic age acceleration, and gut-brain axis (gut microbiome/neurobiological) dysregulation. Because environmental factors (e.g., nutrition, stress) impact gut microbes, the gut-brain axis may be a critical point at which poverty affects the body and a key pathway in the perpetuation of health disparities. Informed by the NIMHD Research Framework, the proposed observational cross-sectional study focuses on experiences of poverty across biological, behavioral, physical/built environment, and sociocultural domains at individual, interpersonal, and community levels to examine the influence experiences of poverty (low SES, early life stress, material needs insecurity) on mental and metabolic health. Data from 2 cohorts of predominantly BHW with lower SES, with and without HIV will be utilized. In Aims 1 and 2, we will leverage data from women (N~2000) who participated in an NIMDH co-funded U01 study. In Aim 1, we will examine relationships among experiences of poverty, epigenetic aging, and mental and metabolic health, and in Aim 2 we will examine relationships among experiences of poverty, the gut microbiome, and mental and metabolic health. In exploratory Aim 3 we will use fMRI data from the NIMH-funded P30 Clinical Outcomes Cohort (N~100 women) to characterize the gut-brain axis by identifying gut microbiome correlates of depression-linked neurobiological substrates. The proposed research and training plan, carefully designed with guidance from an experienced, well-funded, and engaged team of expert mentors, will allow the candidate to build on prior expertise and fill training gaps in 1) integrating multiple -omic (epigenetic, microbiome) approaches, 2) bioinformatic approaches to analyzing the gut microbiome, and 3) utilizing neuroimaging data to examine brain-based symptoms. Closing these scientific and training gaps will augment the candidate’s strong trajectory toward independence and set the stage for a longitudinal R01 examining the epigenetic aging and microbiome effects of poverty on mental and metabolic health in HIV to identify objective, sentinel indicators to prompt timely clinical action—an urgently needed step toward eradicating health disparities for BHW and advancing healthy outcomes for all.

NIH Research Projects · FY 2026 · 2024-08

Modified Project Summary/Abstract Section As the U.S. population ages, the number of individuals living with dementia is rapidly increasing, making the identification of modifiable risk factors more urgent than ever. Recent research, including our own studies, suggests a significant association between poor oral health, such as periodontitis and tooth loss, and an increased risk of developing dementia. However, the specifics of how poor oral health influences dementia, particularly its association with two different subtypes of dementia, like Alzheimer’s disease (AD) and vascular dementia (VaD), remain largely unexplored. The systemic inflammatory responses caused by periodontal diseases will lead to the formation of amyloid-beta peptides (Aβ) and intraneuronal neurofibrillary tangles, contributing to the progression of AD. Our prior study also found subgingival periodontal bacteria at both genera and species levels is associated with reduced cerebrospinal fluid Aβ-42. However, the biological mechanisms underpinning the relationship between oral health and different subtypes of dementia are understudied. It is also unknow how social factors impact the link between oral health and dementia. Our study aims to address these knowledge gaps by analyzing multiple extensive population-based datasets, including National Health and Nutrition Examination Survey (1988-2018) and Health and Retirement Study (2006-2020), both linked to Medicare claims data (available up to 2021), Baltimore Longitudinal Study on Aging (2004-2021), UK Biobank (UKB, 2006-2021), and Genome-Wide Association Study datasets (i.e., UKB, FinnGen Biobank, and MRC Integrative Epidemiology Unit). These datasets offer a comprehensive view, encompassing large, comprehensive samples, and providing unique insights into the association between oral health and dementia. Our specific aims include Aim 1: Establish the relationship between poor oral health and clinical biomarkers of dementia (Aβ, tau proteins, and brain atrophy) and AD or VaD. We hypothesize that poor oral health will be associated with the presence of clinical biomarkers of dementia and incident AD or VaD. Aim 2: Test the three hypothetical biological pathways (i.e., systemic inflammation, accelerated biological aging, and dysbiotic oral microbiome) linking oral health with AD and VaD. We hypothesize that systemic inflammation and accelerated biological aging will mediate the association between poor oral health and incident AD or VaD. We will conduct exploratory analyses of the links among poor oral health, dysbiotic oral microbiome, and AD/VaD. Aim 3: Determine potential moderators that can influence the relationships of poor oral health with AD and VaD. Key social factors will be included as moderators. We hypothesize that females and those with lower social support and higher social isolation, residing in impoverished areas, would face higher AD/VaD risks with poor oral health exposure. The findings will illuminate the causal relationship between poor oral health and dementia, guide targeted interventions to reduce dementia risks, and contribute to dementia prevention and control strategies including early diagnosis, management, treatment, and genetic counseling.

NSF Awards · FY 2024 · 2024-08

Computer chips specialized for specific applications, or application-specific integrated circuits (ASICs), make applications dramatically faster and more energy efficient than when the same application runs on general-purpose processors such as those in laptops and desktops. The US CHIPS Act envisions that power and energy gains from ASICs will "enable startups and researchers to rapidly innovate at a lower cost," enabling new biomedical devices, faster gene sequencing, new cryptographic protocols to protect user privacy, and many other "killer" applications of the future. However, chip design know-how is highly specialized, usually taught in Computer Engineering departments using specialized software tools often inaccessible to students and researchers in other departments and schools. Thus, researchers in disciplines that stand to greatly benefit from learning chip design have limited opportunities to access this knowledge. This National Science Foundation Research Traineeship (NRT) award to New York University (NYU) will introduce an innovative new PhD traineeship model involving tailored chip design coursework, high-impact interdisciplinary research projects pairing domain experts with hardware experts, and a “Silicon Makerspace” that makes chip design software and hardware accessible and available to students across the university. The outcomes of Chips4All will enable fundamental new innovations in the domain sciences. To engage the broader community, Brooklyn Chips Summit (BRICS), a 3-day event of talks by leading experts in chip design, seminars, and panels will be organized and open to the general public. The project anticipates training 308 trainees, 250 MS students and 58 PhD students, including 24 funded PhD trainees, from across schools at departments at NYU including Electrical and Computer Engineering, Computer Science, Biomedical Engineering, Chemistry, Physics, Mathematics and the School of Medicine. As Moore's law flags, the semiconductor industry is increasingly focused on tailored ASICs that can unlock orders-of-magnitude improvements in performance and energy efficiency over traditional general-purpose programmable processors. To realize this vision, Chips4All seeks to train experts who possess both deep domain knowledge and hardware design skills and can collaborate in interdisciplinary teams to realize transformative ideas in silicon. Chips4All aims to democratize hardware design skills across disciplines, creating a replicable model for cross-disciplinary hardware education in the U.S. The novel aspects of the traineeship model are (1) Accessible Chip Design Curriculum, (2) High-Impact Convergent Research, (3) Practical Experience in Chip Design concluding in a chip tapeout, and (4) Establishment of a Silicon Makerspace, easily accessible to all NYU students, faculty, and staff that seeks to build a community of users around hardware design. The Chips4All project has several avenues for transformative societal impact including significantly improving the diagnosis and treatment of genetic disorders and cancers, portable and wearable assistive technologies, brain monitoring techniques to help address mental health and neurological diseases and enhanced user privacy in cloud computing. All Chips4All team members are committed to a diverse and inclusive environment for Chips4All trainees. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Gender stereotypes—including related to science, math, and intellectual potential more generally—take root in early childhood and contribute to immediate and long-term gender disparities in science engagement and achievement. The goal of this project is to reveal the processes underlying the development of such stereotypes in early childhood, while at the same time developing new approaches to reduce stereotype acquisition. The project tests the hypothesis that subtle but powerful features of how parents talk about gender with young children contribute to the development of a general, abstract belief that gender leads to fundamentally different kinds of people. In this way, language that contributes to the abstract expectation that boys and girls are fundamentally different from one another facilitates stereotype acquisition, even if the language itself does not include any stereotypic content. The present project tests this conceptual model and compares it to theoretical alternatives via an intervention design that targets different aspects of parent language and knowledge. This project will test whether targeting the language that young children hear about gender can reduce the development of gender stereotypes over time, while also addressing fundamental questions about how beliefs are spread across communities through subtle features of language. This project will conduct an experimental intervention study with longitudinal follow-up involving young children (ages 3-4) and their parents. The experiment involves unmoderated remote, intervention research that facilitates participation across broad and diverse populations of families. The intervention targets mechanisms in parent-child conversation that facilitates the acquisition of social stereotypes in early childhood. Families of approximately 450 children will be randomly assigned to one of three experimental conditions, targeting either the (a) linguistic structure of parents’ references to gender, (b) parents’ knowledge about stereotype development, or (c) a control condition. Then, the project will remotely document parent-child conversations referring to gender and code the conversations for features of content and linguistic structure using cutting-edge artificial intelligence methods. They will then chart the trajectory of children’s language related to gender stereotypes about science, math, and intellectual ability over eighteen months. This research will reveal how language contributes to the development of gender stereotypes over time, while at the same time identifying new intervention approaches for disrupting their acquisition. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This research examines the interconnectedness of residents from varied neighborhoods, using smartphone-derived mobility data, including mobility networks of 45 million devices that cover 220,000 census blocks. The research explores how residents from different neighborhoods engage with other communities, how these interactions change over time and space and vary with neighborhood socioeconomic and demographic characteristics, and how neighborhood mobility networks and the nature of connections between neighborhoods are associated with measures of community well-being. Broader impacts of the research include data transparency and accessibility, robust training opportunities for junior researchers, diverse educational and experiential learning pathways for students, and the development of tools and indicators for urban planning, policy, and community-led support. Using large-scale mobility data and a range of ancillary land use, socioeconomic, and demographic data, together with machine learning and network analysis methods, this project explores how neighborhood mobility networks and the nature of connections between neighborhoods are associated with measures of community well-being, and whether time-varying network structures can predict neighborhood change. Specific objectives of the research project are to: (1) identify the nature and extent of representativeness bias in mobility data, and develop methods to correct for observed biases; (2) model neighborhood connectedness networks using mobility data and analyze changing network structures over time and in response to various exogenous shocks; (3) identify and define community and neighborhood boundaries as a function of connectedness observed through mobility behaviors; and (4) utilize community networks to predict neighborhood change and evaluate neighborhood integration and segregation as a function of mobility behavior between communities. 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.