New York University

NSF Awards · FY 2024 · 2024-08

New York University, Northwestern University, and the University of Pennsylvania form the Critical STEM Faculty Alliance (C-STEM). Leveraging their combined strengths, they aim to develop an infrastructural technology system that provides more opportunities and lowers systemic risks for historically underrepresented groups. C-STEM will examine college and university functions, to understand how to train effective technology researchers and teachers from these groups. NSF emphasizes creating opportunities everywhere. Accordingly, C-STEM seeks to help new researchers from underrepresented backgrounds build strong professional networks, establish stable pathways that advance careers, and collaborate with other experts in academia, industry, and government. Also, C-STEM aims to help researchers build new projects and design innovative educational tools to improve people's lives. Its goal is to ensure that technology serves the public interest, especially those who have been most negatively affected by technology. C-STEM aims to design and implement institutional self-assessments at the three C-STEM Alliance institutions. The alliance will prioritize collecting and analyzing data to identify inequities affecting underrepresented minority (URM) doctoral students, postdoctoral scholars and early career faculty in STEM fields. To assess the need for the C-STEM Alliance, the project will collect data on the demographic representation (race, ethnicity, national origin, sex, gender, first-generation status) of doctoral students and faculty in STEM and related fields. The project will also conduct curriculum surveys to understand demographic and socio-technical content representation in STEM courses, and review research production by minority and non-minority STEM students and faculty. Surveys will also evaluate existing mentorship and support structures, and collect data on minority STEM doctoral student outcomes, such as degree completion and post-degree hiring. Additionally, the alliance will gather qualitative data from minority STEM students and faculty about their experiences. This data will help identify institutional challenges and justify the alliance's activities, demonstrating how they address specific needs. To assess institutional readiness, the project will collect data that include reviews of diversity commitments by university leaders and progress towards diversity, equity, and inclusion goals. This will demonstrate C-STEM institutions’ commitment to increasing the representation, resilience, and success of minority doctoral students and faculty in STEM. The alliance intends for this work to help research communities better understand the incentives and affordances institutional leaders’ encounter in their efforts to create, continue, or expand key structures, such as postdoctoral programs and frameworks for transitioning postdoctoral scholars to tenure-track positions. 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

Recent advancements in data-driven decision-making have transformed fields such as healthcare and digital marketing. These areas now prioritize understanding the long-term consequences of actions and policies. For example, drug design aims to mitigate long-term side effects without compromising immediate efficacy; online recommender systems like Netflix aim to improve long-term customer benefits while balancing them with short-term engagement metrics. Evaluating long-term effects is challenging due to the dynamic nature of environments and the cumulative uncertainty of future predictions, especially in real-life scenarios that require prompt decisions. This grant supports research to develop innovative statistical inference methods and models for estimating long-term effects efficiently and effectively. The PI will integrate research and education by involving graduate students in the research and incorporating findings into mini-courses at workshops. The project will also provide mentoring and support for URM graduate students and postdocs, fostering a diverse and inclusive research community. In more detail, this project proposes several research thrusts that provide various models to capture long-term effects in real-life scenarios. The first thrust focuses on environments with time-homogeneous transitions, assuming a Markovian framework. The main goal is to use system observations to understand dynamics, establish robust estimators, and quantify uncertainty. The methods are expected to handle distributional shifts in data, misspecification in function approximation, and the potential high-dimensionality in models. The second thrust concerns non-stationary dynamic systems. Challenges include determining the change points as the system evolves, selecting the most useful and related data, and using an appropriate surrogate index approach to form a valid estimate. On top of the first two thrusts, the third one involves integrating multiple datasets to facilitate estimation. The goal is to develop methods that combine relevant but non-identical data sources effectively to mitigate the issue of data scarcity. 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 · 2024-08

Morbidity and mortality rates from type 2 diabetes (T2D) are high and continue to increase, with only 32% of adults who have T2D meeting targets for glycosylated hemoglobin (A1c). Immigrants and racial/ethnic groups have worse rates of achieving the target A1c goal. Self-management is critical in meeting glycemic control. Haitian immigrants have unique migration experiences compared to other ethnic groups that may impact their T2D self-management. Our preliminary work reveals differences in Haitian immigrants dietary and exercise habits compared with their practices in Haiti. Haitian immigrants also have higher A1c levels compared with African Americans and Cuban Americans. The purpose of this application submitted in response to PA-18-129 is to characterize self-management behaviors, barriers to T2D self-management, and glycemic control in adult Haitian immigrants. Our aim is to reduce health care disparities by developing a T2D self-management education intervention specific to adult Haitian immigrants. Data from the K99 phase will inform the development of a diabetes self-management education (DSME) program to be carried out during the R00 phase and establish feasibility, acceptability, and preliminary efficacy. Our aims are to: 1) Describe self-management behaviors of 100 adult Haitian immigrants with T2D as measured by multiple methods (diabetes self-management survey, 3-day diet recall, blood glucose level, physical activity via accelerometer, and pill counts) and their correlations with glycemic biomarkers (A1c and continuous glucose variability via continuous glucose monitoring (CGM); 2) Describe barriers to self-management in these 100 adult Haitian immigrants with T2D including socio-demographic status, health status (comorbidity), psychosocial factors (cultural health beliefs, acculturative and discrimination stress), and environmental factors (access to care, food insecurity) using mixed methods (quantitative surveys and interviews with a subsample); 3) Use community-engaged approaches with 10 adult Haitian immigrants with T2D to develop a DSME program to reduce barriers and improve self-management and glycemic control; and 4) Conduct a randomized pilot study with 60 adult Haitian immigrants to establish feasibility, acceptability, and preliminary efficacy of the DSME program compared to standard care. We will use descriptive approaches for the K99 and aim 3 of the R00 phase. For aim 4, we will use a randomized pilot study design. A hip actigraphy measures of physical activity, glycemic biomarkers from A1c and glucose variability via CGM, self-report, objective, and interview measures of self-management will be collected. Data will be analyzed using multiple regression, content analysis techniques, and preliminary effect sizes will be generated from aim 4. The project is significant in its potential to enhance understanding of T2D self-management and glycemic control in this immigrant population at high-risk for negative T2D health outcomes. This study addresses NINR’s key theme, “Self-Management of Chronic Conditions”. The accomplished mentor committee and the candidate’s ongoing research relationships in this field will create the ideal setting for these investigations.

NSF Awards · FY 2024 · 2024-08

Animals are covered in microbes. For example, the microbes in and on the human body number in the trillions, outnumbering our own cells. Sea turtles, whales, dolphins, and manatees represent some of our most cherished marine megafauna, and these, too, are covered in diverse microbes. These microbial communities are essential to protecting these animals from infections and disease and include bacteria and diatoms, a group of microscopic algae that produces 20% of Earth’s oxygen. Microbes represent most of Earth’s biodiversity, but most of these species are yet to be discovered. This project will document the diatom and bacterial species living on marine megafauna, describing and naming many new diatom species for the first time. Genome sequencing and characterization of the chemical environments will show how the diatoms are related to one another, how they move between host animals, and how they interact with bacteria to use the unique resources offered by their hosts. Since many of these diatoms reside only on threatened or endangered animal species, the fates of these microbial communities are tied to their hosts. The discoveries made through this project may allow researchers to grow these diatoms in the lab, creating a permanent living record should their hosts suffer extinction. The project will provide cross-disciplinary training for students, preparing them for jobs in industry or academia. A documentary film will introduce the complex microbial communities studied in this project to a general audience. Through this project, an estimated 50 diatom species will be documented and described, doubling the number of known epizoic diatom species. Genome sequencing of cultivable and uncultivable species will be used to place epizoic diatoms onto the broader diatom phylogeny, which will provide a framework to: (1) infer how many times free-living ancestors have colonized and diversified on animal hosts, (2) reveal patterns of host switching to discern epizoic generalist and specialist diatom species, and (3) characterize mechanisms of host specialization in one model lineage that speciated and underwent a radical trophic shift following a host transition. The broad phylogenomic framework, combined with complementary metagenomic, metatranscriptomic, and metabolomic profiles will reveal the role of host skin microbiome in shaping trophic strategies and other adaptive changes by diatoms, factors that likely combine to facilitate or constrain host switching and speciation on evolutionary timescales. 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

Recent strides in artificial intelligence (AI) have demonstrated impressive capabilities in assisting with a wide range of text-generation tasks. One promising use-case is the use generative AI methods to assist in developing software and hardware. Large Language Models (LLMs) are already in use to assist in software code generation, and recent research has demonstrated promise of LLMs in assisting in various stages of computer chip design. To further explore and encourage research in this rapidly advancing field, the inaugural International Symposium on LLM-Aided Design (LAD’24) seeks to bring together leading researchers from academia, industry and government and create a research community around this field. Graduate and undergraduate student researchers will be a critical part of this community, and this award will support travel for students with limited or no financial support to travel to LAD’24 in Almaden California. Large Language Models (LLMs) are making significant strides in text and content generation for a wide range of tasks, and are proving to be helpful in developing software/hardware computing stacks. These advancements are poised to transform software and hardware code generation, system-level architecture design, electronic design automation flow, and test and verification processes. LAD’24 will capitalize on recent generative AI and LLM technology innovations, introducing new methods and solutions for design automation across various applications. It aims to establish itself as a leading forum for discussing how LLMs can enhance quality, productivity, robustness, and cost-efficiency in circuit, software, and computing systems design. This award will support up to ten graduate student researchers to attend LAD’24, allowing them to share their research and findings with academic and industry leaders in this nascent field. 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

The goal of this project is to investigate the impact of extreme cold and arid climatic conditions on ancient humans. Aridity is an important element of current human-induced changes in our climate, affecting large parts of the populated world. Initially ignored by scientists and policymakers, the ways in which mobile, small-scale societies adapted to such environmental changes in the past have proven insightful for designing solutions for the future. The deep past offers multiple examples of arid events, but few are extreme enough or lasted sufficiently long to act as likely drivers of large-scale changes in human behavior. One such event is the Last Glacial Maximum (LGM, 26-19 thousands of years (ka) ago), a cold and dry period that lasted for several millennia and provoked large worldwide ecological shifts. Hunters and gatherers of that time either abandoned the affected regions or survived in one of two possible ways: by finding local refugia where conditions were milder or, alternatively, by adapting to the increasingly challenging environments through innovations in technology, subsistence, and land-use. Each of these possibilities offers a different piece of the puzzle of human adaptation to climate change and the present research will help decide between these alternatives using archaeological data. The project will also serve as a training platform for American and local students and as a vehicle for archaeological conservation and education efforts. The PI excavates a recently discovered archaeological site, which preserves a unique record of human presence at the height of the climatic deterioration associated with the LGM. The team combines a larger-scale excavation with studies of fossil plants and animals to understand local responses to global climate change, as well as the diversity of sources of food and fuel available to humans during this period. In particular, the spectrum of plants are compared with the charcoal remains to study which plants humans picked for making fires inside the cave, an important adaptation to the cold. The scientists analyze the spatial distribution of exploited stone raw materials, which can reveal how ancient people moved through the landscape and how they organized their technologies. Together, these analyses will reveal the role of risk minimization and mobility as behavioral responses to falling temperatures and intensifying aridity. 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

Machine learning (ML) tools and Large Language Models (LLMs) have shown great promise in making the complex task of designing a computer chip design easier, faster and better. However, modern ML tools and LLMs rely on vast quantities of high-quality training data. Unfortunately, much of the data that can be used to train ML models for chip design are held by semiconductor companies who cannot release this data publicly. The paucity of large and good datasets poses a fundamental challenge to progress in this area. Such a challenge was encountered during the early years of the ML boom. At the time, a public dataset of images, ImageNet, played the role of catalyzing progress in the area of ML for image recognition. To mirror this effort for chip design, this project will convene leading experts from academia, industry and government at an “Imagenets4EDA” workshop. Via a sequence of panels, talks, and brainstorming sessions, the goal is to put forth a concrete agenda on how industry, academia and government can work together to achieve the common goal of building the equivalent of the ImageNet dataset for chip design. The outcome will be a concrete action plan that participants will commit to pursue. The organizers will make every effort to ensure that a broad range of voices, including participants from under-represented groups, are invited and heard at the workshop. Participants will also be encouraged to think about ways to achieve geographic, institutional, and demographic diversity in the group of students and researchers involved in data collection efforts and benchmarking competitions. The project will fund the participation of US-based researchers and students in the workshop. Recently, ML methods, like generative AI, LLMs and reinforcement learning (RL), have shown remarkable ability in performing a wide range of tasks in hardware design. Their applications in the design of computing stacks promise to revolutionize hardware code generation, system-level, and the electronic design automation (EDA) flow. Yet there is a critical need for datasets and benchmarks to realize this promise. By bringing together a community of experts in this area representing all key stakeholders, the ImageNets4EDA workshop agenda will pursue three synergistic goals. First, gaps in existing datasets will be identified via discussions and analyses of existing datasets, and pinpointing areas where current datasets are lacking. The second goal is an open call to the community---academia, industry and government---to contribute datasets in ways that protect the intellectual property rights of companies, while still providing sufficient quality of data that would enable training of foundation models for EDA. The third direction is a plan to organize benchmarking competitions by deciding one step in the EDA flow, for instance, physical design. ML tools will cut chip design lifecycles, improve productivity of semiconductor designers, and result in faster and lower chips, providing large benefits to US economy and society. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

When people plan for the future, they often do so under uncertainty. Whether deciding a morning commute, saving for retirement, or planning for college, people must make decisions about the future while adapting to randomness in their environments. Yet despite the fact that uncertainty is nearly pervasive in real-world planning, little is known about how it affects their plans. This project studies how people change their planning behavior in response to varying levels of randomness in their environment. The project addresses this question by analyzing human behavior and eye movements to develop computational models of how people plan and act under uncertainty. Predictions are tested on multiple sources of uncertainty, including uncertainty in reward, uncertainty in actions, and volatile environments. Developing a deeper understanding of how uncertainty affects planning behavior has a wide range of potential applications, not just for basic science, but also for building effective interventions to help individuals and groups tackle problems for longer decision horizons. Multi-step planning is a hallmark of decision making. Every day, people are faced with tough decisions about the future, for example individually on career paths and collectively on large-scale societal problems. In real-world planning, these decisions often come hand in hand with some level of unpredictability or uncertainty. Studying how people adapt to this uncertainty is particularly interesting in cognitive science because it can give insights to the limits of human cognitive capacity; planning in uncertain environments involves making tradeoffs between cognitive load and maximizing reward while considering several possible future trajectories. Yet this effect has often been overlooked in the field. The central question of this proposal is how people change their planning when confronted with stochasticity. A combination of human behavior, eye measurements, and computational modeling are used to address this question. Computational models serve to identify the cognitive mechanisms leading up to a decision, while also producing predictions for eye fixations and eye movements. Throughout the proposed work, three forms of stochasticity are considered in parallel: unreliability, volatility, and transition noise. The proposed work is expected to substantially expand the field’s understanding of multi-step planning, as well as of the role of eye movements in planning. 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 · 2024-08

Project Summary / Abstract Gene families include up to hundreds of highly similar sequences that may perform related but non- redundant functions. Therefore, understanding how the correct gene is activated at the appropriate location and time remains an important question. Odorant receptor (Or) genes present a particularly prominent example of this problem. Previous research in mice (which have >1,000 Ors) and flies (which have 60 Ors) led to an idea that large Or families employ stochastic gene choice, while small Or families follow deterministic specification. The jumping ant Harpegnathos saltator lies in between these extremes, possessing 376 Or genes, most of which are not interspersed throughout the genome, but located within tandem genomic arrays that contain up to 58 genes each. This proposal will test a model where each olfactory sensory neuron (OSN) in ants expresses a single locus, either a single gene or an array, using the same deterministic mechanism as Drosophila melanogaster to choose between a limited number of loci. Single gene loci are expressed deterministically, while loci that contain multiple genes have an added stochastic mechanism by which a single Or promoter is chosen out of the array. To this end, I performed single-nucleus RNA-seq (snRNA-seq) on the H. saltator antennae. Strikingly, although isolated genes were uniquely expressed in non-overlapping sets of OSNs, genes within arrays were co-expressed. In all such cases, they followed a highly stereotypical pattern: given genes A, B, C within an array, either C (the most 3’ gene) was expressed alone in some OSNs, or B and C were co-expressed in another subset of OSNs, or all three genes were expressed concurrently in yet another subset. In addition, the snRNA-seq data revealed extensive antisense transcription in these loci. The antisense RNAs covered genes upstream of the first transcribed gene and appeared to originate from the same promoter. Thus, the antisense RNAs are mutually exclusive with the sense RNAs, suggesting that the antisenses may repress the transcription of genes upstream of the chosen promoter. In aim 1, I will investigate the mechanisms of the co-transcription among clustered genes, determine whether co-transcribed genes are co-translated and whether OSNs that express them target distinct glomeruli in the brain, identify sequence features that enable transcription and provide stability to the antisense RNAs, and test the potential repressive function of the antisense transcripts. Together, this will provide a comprehensive description of the transcriptional mechanisms of the single gene choice in H. saltator Or arrays and uncover how this translates into protein expression. In aim 2, I will determine the transcription factor code associated with each Or gene, collect chromatin interaction and multiome (snRNA-seq paired with ATAC-seq) data to comprehensively identify putative regulatory regions, determine whether one or both alleles are expressed in each cell, and test the function of the candidate enhancers. Together, this will uncover the regulatory architecture of the Or loci, potentially revealing a novel stochastic mechanism of promoter choice within arrays.

NIH Research Projects · FY 2026 · 2024-08

ABSTRACT/PROJECT DESCRIPTION: Technologies for gene delivery are desperately needed to address a wide array of pathological processes in the various tissues of the body. In particular, effective therapies for diabetic skin wounds poses a significant clinical and scientific challenge due to heightened health care costs and increasing incidence of diabetes worldwide. Recent progress in biomaterial-based gene delivery has ushered in promising options to treat chronic wounds via localized effects; however, degradation or interception of precious nucleic acid therapeutic cargo in transit— especially after unsuccessful escape from their endocytic vesicle—contributes to underwhelming transfection efficiencies that render these technologies insufficient for clinical translation and an unmet need. The overall objective of this proposal is to construct a modular arsenal of versatile, multi-functional supercharged proteins and lipids that can be co-formulated into a hybrid nanovehicle, termed lipoproteoplex (LPP), for the delivery of short interfering RNA (siRNA) sequences. We seek to improve the efficiency and safety of the LPP technological platform by delving into its unique mechanism of cellular entry and cytosolic uptake. We propose the central hypothesis that engineered proteins serve as the core, functional component of the LPP, dictating cargo binding strength, whereas the outer lipid component serves to protect the payload. Both items collectively contribute to and influence the bulk LPP's mechanism of cellular entry and cytosolic uptake. By harnessing a computationally- informed experimental approach, we will generate novel cationic supercharged protein sequences and study their interactions with various lipid shells to enhance the overall effectiveness of our LPP platform technology for siRNA delivery. We will pursue this optimized formula through the following specific aims: (1) expand the cationic supercharged protein library with viral tagged mutants to maximize the amount of endosomal escape while balancing gene binding capabilities; (2) evaluate the role of the LPP's outer liposome on payload protection and vehicle self-assembly; and (3) elucidate the LPP's cellular uptake mechanism essential for efficacious delivery of siKeap1 in a murine humanized diabetic wound model. The expected outcome of this proposal is an adaptable LPP formulation optimized to address the critical disease model of diabetic ulcers and wounds by promoting wounded skin repair in a pre-clinical hyperglycemic environment. Beyond the proof-of-concept validation model, the long-term goal of our work is to rationally design the LPP formulation for other siRNA sequences to have a positive translational impact on a wide array of monogenic cutaneous disorders. While other approaches focus on chemically modifying the vehicle, our innovative approach focuses on the LPP's easily-modulated and scalable components as the key driver of nucleic acid loading and subsequently successful delivery. The proposed research will determine significant structure-property-function relationships in the LPP's protein and lipid components, which are quintessential to designing a harmonized LPP formula that can overcome the endosomal escape barrier and achieve ultraefficient cytosolic siRNA delivery.

NIH Research Projects · FY 2025 · 2024-08

Among the 6.5 million adults with Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRD) in the United States, nearly a third of persons living with dementia (PLWD) receive care in the home healthcare setting, many of whom are at the end of life with advanced dementia. Many often benefit from transition to hospice when eligible and aligned with goals of care. Despite the benefits of hospice care for PLWD such as improved care quality, fewer hospitalizations, and increased care partner support, PLWD have differentially low rates of hospice use. Some high risk and high need populations have higher hospitalizations and low hospice use at the end of life. End-of-life care research has demonstrated that personal preferences, values, beliefs, and lack of access to hospice care may explain some differences in hospice use among PLWD. As care partners navigate hospice-related decision-making, dementia-tailored care management has potential to improve end-of-life care for PLWD and care partners. However, gaps in evidence preclude the development of interventions to improve hospice transitions for PLWD and their caregivers. Care management interventions informed by end-users, such as home healthcare professionals and care partners, that can be integrated into existing care delivery models, are urgently needed. Additionally, home healthcare is a crucial intervention point for increasing hospice use; however, care management that prioritizes end-of-life medical and social needs of PLWD and care partners requires further study in this setting where many PLWD and care partners navigate care at the end of life. Therefore, this K23 Mentored Patient-Oriented Research Career Development Award will provide the candidate, Komal Patel Murali, PhD, RN, ACNP-BC, with training in co-design research and AD/ADRD intervention development and clinical trials, research with high risk and high need populations, and professional and career development to progress on a pathway to independence as an interdisciplinary nurse scientist and aging researcher focused on improving end-of-life care for PLWD and their care partners. The specific aims for this career development award are to: 1) Co-design a care management checklist to guide hospice transitions for PLWD and their care partners receiving home healthcare, 2) Pilot test the hospice transitions checklist within usual care management for feasibility, acceptability, fidelity, and usability for PLWD and care partners and HHC professionals, 3) Examine hospice enrollment, time to enrollment, and care partner satisfaction at 1-month and 6-month follow up with care partners. This study is conceptually guided by the NIH Stage Model for Intervention Development. The intervention will be delivered within usual care management surrounding hospice transitions within a large home healthcare agency in New York City and prepare the candidate for submission of a subsequent larger R01 study examining the intervention’s efficacy and effectiveness.

NIH Research Projects · FY 2026 · 2024-08

We propose a multi-scale (from neurons to regions) theory that enables us to analyze neural computations from large sets of neurons engaging in a variety of simple to complex tasks. Advances in recording techniques in neuroscience have enabled simultaneous recordings of a large number of neural activities, providing greater access to signals in the brain, but also presenting a challenge in analyzing these high-dimensional neural activities in an interpretable way. Recently, we have developed a theoretical framework, which we call the Manifold Capacity Theory (MCT) framework, to analytically connect the geometric structure of neural activities to the capacity of a task-implementing readout. This work provides a new theoretical framework and data analysis algorithms to measure the efficiency of neural population data on representing the stimuli invariantly, and on implementing a given task. In Aim 1, we will use the MCT framework to characterize how properties of single neuron tuning curves collectively shape the geometry of neural manifolds. In particular we will focus on properties such as the number of tuning curves, and the maximum firing rate of tuning curves, as well as distribution properties such as tuning heterogeneity. Next, in Aim 2, by utilizing machine-learning based neural network modeling methods and new geometrical analysis framework, we will develop a new modeling paradigm ideal for answering how neural representations become transformed across brain regions or layers of the circuit hierarchy, and across acquisition of a task, both in biological brains and neural network models. Our preliminary analysis shows that geometric frameworks can produce population-level hypotheses on different mechanisms employed by different network architectures and employed by acquisition of a task. Finally, in Aim 3, we utilize measures from Manifold Capacity Theory as design principles for developing artificial neural network models of the brain. Combined together, these studies will lay groundwork for using geometrical frameworks and machine learning tools for (1) describing high-dimensional neural data across multiple spatial and temporal scales, (2) testing hypothesis on geometric mechanisms underlying neural circuit motifs and learning rules in shaping manifold representations, and (3) building novel algorithms for generating brain models and machine-generated hypotheses. In summary, the goals of this proposal are to develop a new class of multi-level framework for analyzing neural representations, by connecting single-neuron structure to population-level geometry to task- level efficiency and validate the new geometric tools using machine-learning generated neural network models as a testbed and hypothesis generator. Accomplishing these goals will lead to new theoretical principles and computational analysis paradigms that can be generalized across multiple spatiotemporal scales, across different modalities and brain regions. This in turn can lead to widely applicable quantitative tools in the broader neuroscience community, for understanding the neuronal basis of implementing behavioral and cognitive tasks in animals and humans

NSF Awards · FY 2024 · 2024-07

The renormalization group method is a cornerstone of modern theoretical physics, explaining a vast range of central phenomena from areas spanning from elementary particle to solid state physics and beyond. The main idea is the analysis of an effective description of a theory at different length scales, leading to a dynamics as the scale varies---the renormalization group dynamics. The mathematics of the renormalization group however is only well understood in very few cases. One of the main goals of this project is to develop mathematical methods and examples of the renormalization group in different contexts. Graduate students wills be mentored as part of the project; the awardee will present courses on relevant material and make their lecture notes available, and also participate in outreach programs for K12 students. The main focus of this proposal is on the use of the renormalization group method in two contexts, the small and large scale properties of stochastic dynamics of statistical field theories, and the mathematics of spontaneously broken continuous symmetries, in particular in the example of the OSp(1|2) non-linear sigma model. In a complementary direction, some examples of integrable quantum field theories will be explored as well. 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 · 2024-07

Computer chips and electronics are the vital backbone of our digital economy. As a strategic priority, the U.S. seeks to dramatically increase the domestic design of production of chips, which is currently being done offshore. Unfortunately, there is a talent gap between the projected job growth in this sector and available talent. The BASICS (Bridge to Application Specific Integrated Circuits) program seeks to provide non-STEM professionals with a self-paced, low-cost and fully-online curriculum to get a headstart in the chip design industry, culminating in a capstone project where students will fabricate and test their own small-sized chip. BASICS is a partnership between the successful BRIDGE program at New York University (NYU) that provides pathways for non-STEM professionals to computer science (CS) degrees and jobs, NYUs Computer Engineering department, and “Zero-to-ASICs” an online curriculum that teaches participants how to design their own chips. Graduating students will learn the basics of algorithms and programming, as well as theoretical foundations of chip design in addition to the chip design capstone. Once completed, the students will be eligible for direct admission to NYU’s Masters in Computer Engineering program, and/or potential positions in the semiconductor industry. As Moore’s law flags, the semiconductor industry is looking towards application-specific integrated circuits (ASICs)—chips specialized for specific tasks, for example, encryption, machine learning, etc.—for continued innovation, unlocking orders-of-magnitude improvements in performance and energy efficiency. The skills required to fulfill these roles range from algorithms, software programming and scripting, to hardware design fundamentals and expertise in hardware description languages like Verilog, and are typically acquired in degree programs in Computer Engineering departments, but are out of reach for non-STEM professionals. Participants in the BASICS program will acquire each of these skills in a focused and online format: programming and algorithms from our existing BRIDGE program, hardware fundamentals including combinational and sequential circuit design from the Computer Engineering faculty, and hands-on knowledge on designing, synthesizing, floorplanning and taping out a chip from Zero-to-ASICs. The culmination is taping out and testing a small less than 1000 gate digital integrated circuit. Graduating students who enter the MS in Computer Engineering program at NYU or our partner institutions can then build on these skills via advanced classes from the transistor level to micro-architecture design, providing pathways to a range of roles in the semiconductor industry. 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 · 2024-07

This project will advance our knowledge of random matrices, which arise from many correlated systems in physics, data science, pure and applied mathematics. While the Gaussian universality class is intimately related to the notion of independence, the random matrix universality class was proposed by Wigner to model stable energy levels of heavy nuclei. The models he introduced have since been understood to serve as paradigms for deep phenomena such as energy level repulsion, eigenstate thermalization, hierarchical self-assembly, disorder in high dimension, and the distribution of the zeros of the Riemann zeta function. This project will provide deeper probabilistic understanding for longstanding problems. The awardee will also engage in a variety of organizational, educational and outreach activities, including conference organization, course development, talks for a range of audiences, and mentoring of students. A new line of research has appeared recently in random matrix theory, establishing a connection with the active part of probability theory which studies logarithmically correlated fields, such as branching Brownian motion and the two-dimensional Gaussian free field. Processes in this class are also intimately related to the Gaussian multiplicative chaos random measures. Some of these connections were made rigorous thanks to the discovery of a hierarchical structure behind random matrices and L-functions. The PI will work on dynamical and branching techniques to discover new statistics, for the following problems regarding random spectra and other complex systems. (1) Branching in integrable random matrix theory and number theory, first in relation to the Fyodorov-Hiary-Keating conjectures, then with studies on a hierarchy behind the multiplicative Fourier transform. (2) Universality of extreme statistics in random matrix theory, with logarithmically correlations for classes of random matrix models. (3) Fisher-Hartwig asymptotics, with the development of probabilistic techniques for the analysis of Toeplitz determinants with singularities and generalizations, and the connection with Gaussian multiplicative chaos random measures. (4) Quantitative universality, with the analysis of the spectral form factor of random matrices, the main observable used in physics to identify the random matrix universality class. 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 · 2024-07

PROJECT SUMMARY/ABSTRACT Precise temporal regulation of stage-specific gene expression is fundamental for the proper development of all organisms. Different molecular mechanisms that ensure the regionalized activation of gene expression, such as activator gradients or localized transcriptional repressors, have been elucidated in remarkable detail; however, the structural features of cis-regulatory sequences that enable transcription factors to achieve the timely deployment of their downstream genes remain mostly unclear. The evolutionarily conserved transcription factor Brachyury provides an example of a regulator that controls the spatial and temporal expression of a large number of genes, many of which are required for the development of the notochord. The notochord is the main feature shared by all chordate embryos, from tunicates to humans. During the early embryogenesis of all chordates, the notochord provides support and patterning signals to the developing body and is indispensable for patterning neural tube, endoderm, paraxial mesoderm, and the structures that they will form. In the tunicate Ciona, an invertebrate chordate, Brachyury is exclusively expressed in notochord cells, where it controls the expression of hundreds of genes. Even though Ciona Brachyury (Ci-Bra) is steadily transcribed and transported to the nuclei of the notochord cells throughout development, the genes downstream of Ci-Bra exhibit a distinctive, temporally staggered onset. In an effort to identify the cis-regulatory strategies responsible for the precise temporal regulation of notochord gene expression by Ci-Bra, we have characterized several cis-regulatory regions (aka enhancers) that control the notochord expression of Ci-Bra- downstream genes characterized by different temporal onsets. These studies have led us to formulate a working hypothesis that explains how Brachyury controls its early-, middle- and late-onset target genes. This hypothesis will be tested through the following experimental approaches: the elucidation of the cis-regulatory principles underlying the sequential activation of early-onset and middle-onset notochord genes directly controlled by Ci-Bra (Aim 1); the functional analysis of two transcription factors that activate expression of late- onset notochord genes (Aim 2); the exploration of the role of transcriptional repressors in notochord development (Aim 3). These studies will shed light on the modalities employed by a pivotal transcription factor to set the temporal context for developmental processes of widespread relevance, such as convergent extension and extracellular matrix secretion, and will provide the mechanistic framework that is needed to diagnose and prevent malformations and tumorigenesis associated with the multifaceted transcription factor Brachyury.

NSF Awards · FY 2024 · 2024-07

Advances in artificial intelligence have the potential to dramatically benefit society, but whether this possibility is realized depends on whether researchers, industry leaders, and policy-makers can ensure that autonomous systems are aligned with human goals, expectations, and cognitive strategies. To this end, this project will develop approaches for training artificial intelligence systems, such as self-driving cars, to make their decision-making processes more transparent and interpretable to people. A distinctive feature of this project is that it will pair human studies with algorithm design to ensure that the computational methods that are developed are informed by an experimentally-grounded understanding of human psychology. In the longer term, this project will bring the fields of psychology and artificial intelligence into closer contact with one another by facilitating the development of shared methodologies and theoretical tools to build trustworthy systems. The goals of this project are twofold. First, it aims to develop a theoretical framework that formalizes human interpretability in terms of the cognitive cost of the simplest mental models that account for an autonomous system’s behavior. The correspondence of different quantitative predictions of this framework with actual human judgments will be validated through a series of rigorously designed behavioral experiments with human participants. Second, the project will develop new deep reinforcement learning algorithms that use the proposed formalism to optimize for human interpretability directly. The key emphasis of the project is to develop novel, psychologically-grounded approaches to human-interpretable machine learning that meaningfully bridge contemporary research in cognitive science and artificial intelligence. 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 · 2024-07

Recently, we have seen extraordinary changes in how information is disseminated and consumed. For instance, algorithms steer individuals to different news and video con tent based on personal characteristics, thus affecting informational similarity or diversity across people. Since rational agents make strategic decisions based on what they think others know and what they will do, these changes in the informational environment can significantly influence the outcomes of various socioeconomic events. For example, information similarity may affect whether citizens in different geographies with different information can coordinate a protest against an authoritarian government or whether investors with private information about a bank’s health decide to run on it. This project studies economic environments in which agents share a common goal but not the same information. It builds the tools to formally study changes in information similarity and how they impact incentives in strategic environments. In particular, it addresses the question of whether and when increases in information similarity facilitate achieving a common goal. The research findings will provide a better understanding of the welfare consequences of the recent changes in informational similarity, and can inform the design of policy interventions. The methodological contribution of the project will be of independent interest outside of social science in statistics. As a first step, the project develops the methodological tools to answer these questions. This involves developing a new order of information similarity that captures the simple idea that as information becomes more similar, conditional on an individual’s private information, she assigns a higher likelihood that others have observed the same information. The literature so far lacked a general measure of information similarity appropriate for incomplete information games. This new notion of information similarity is applied to study classic collective action problems in which individuals want to achieve a common goal, but also want to free-ride. Examples of collective action include protests, regime changes, or voting in committees. An individual may privately learn that reaching the goal is socially beneficial but may still not take the costly action. Her decision depends on what she believes about the information that others have and thus what they will do. The fundamental observation is that greater similarity of information among agents, even those with identical preferences, can act as a double-edged sword. On the one hand, if people believe that others are more likely to have the same information as them, they may be able to coordinate better to reach the common goal. On the other hand, the temptation to free-ride may be exacerbated: If an agent knows that others have the same information and predicts that they will take action, then she does not need to take a costly action herself. The project characterizes precisely when more similar information across individuals helps participation in collective action problems: In particular, more similar information facilitates (impedes) achieving a common goal when achieving the goal is sufficiently challenging (easy). This insight is applied to show why resilient regimes face larger protests while weak regimes face smaller protests as information becomes more similar, and why diversity in committees is beneficial when each vote carries more weight. Finally, the project studies the effect of increased information similarity on behavior in classic coordination games like a bank run. A key methodological result is an equivalence between increasing the similarity of information and expanding the set of equilibria in a class of coordination games. 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 · 2024-07

Project Summary/Abstract There is considerable evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply operations of the same form, but we lack a theoretical framework for how such canonical neural circuit computations can support a wide variety of cognitive processes and brain functions. Through the proposed research, we aim to provide one. Preliminary results demonstrate that a family of circuit models, called Oscillatory Recurrent Gated Neural Integrator Circuits (ORGaNICs), simulates many key neurophysiological and cognitive/perceptual phenomena. We propose to develop models of the dynamics of attention, and working memory, and to test those models with previously published datasets acquired with a wide range of methodologies: human behavioral data, neurophysiological data from primate and rodent prefrontal cortex (PFC), electrophysiology and Ca2+ imaging data from rodent prefrontal cortex, and electrophysiology data from rodent medial entorhinal cortex. In Aim 1, we hypothesize that normalization is critical for the stability and robustness of the recurrent circuits that underlie working memory. We will test this hypothesis by developing an analytical theory, based on ORGaNICs, of delay-period activity, and fitting published measurements of response dynamics in PFC. In Aim 2, we hypothesize that behavioral performance during working memory tasks is limited by trial-to-trial variability in delay-period activity, and also that top-down signals from working memory circuits provide the attention-control signals that modulate sensory activity in visual cortex. We will test these hypotheses by developing an analytical theory of attention and working memory, combining a visual cortex model and a PFC model, and using it to fit previously published measurements of behavioral performance from a variety of attention and working-memory experiments. In Aim 3, we propose to develop and test a theory of manipulation in working memory, with application to navigation, specifically using ORGaNICs to model the responses of populations of head-direction cells while animals are performing the active place avoidance task. We hypothesize that head-direction cells in MEC operate like a working memory representation, by encoding a “landmark” (a sensory feature) relative to the animal's current head direction, and then updating/manipulating the representation of that landmark as the animal's orientation changes. We will test key predictions of the theory with new experiments. The proposed research has the potential to be transformative by: providing a new set of analytical results and computational (software) tools for characterizing and simulating a broad range of neural circuit models, which will impact experimental design and data analysis; making new experimentally-testable predictions for both ORGaNICs and alternative models; and testing some of those predictions with new experiments.

NSF Awards · FY 2024 · 2024-07

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, and partial co-funding from the Chemical Mechanism, Function, and Properties Program, Dr. Claudia E. Avalos and her group at New York University are designing and characterizing molecules intended for applications in quantum sensing and nuclear magnetic resonance (NMR) signal enhancement. Molecules that can be classified as good quantum spin sensors often exhibit properties that also make them good candidates for NMR signal enhancement. In both cases, the molecule’s spin state should be readily controlled by the application of some form of electromagnetic radiation. Using a combination of computational and experimental techniques, the Avalos lab is seeking to identify the magnetic and structural motifs that facilitate optically induced spin control. Identification of classes of molecules that exhibit these properties would have a significant impact on improving the performance of magnetic resonance methods (which are widely used in chemical industry) and quantum sensor designs (with applications such as gyroscopes, magnetic field detection, and nanothermometry). Dr. Avalos is also engaged in educational activities involving magnetic resonance methods at both the local and national level through workshops that aim to help educators explain and incorporate magnetic resonance tools into their physics and chemistry curriculum at the high school and undergraduate level. The ability to optically generate highly polarized nuclear spin states with chromophore-radical (C-R) dyads has the potential to enable highly sensitive multi-dimensional NMR with sample-limited systems without the need for cryogens or expensive microwave sources. Given the vast chemical space that is possible in C-R systems, combined computational and experimental studies are vital to aid in the rational design of C-R molecules with desired electronic and spin properties. Dr. Avalos is seeking to determine how the C-R structure is correlated with the mechanism of polarization of the radical as well as how this structure affects the polarization transfer mechanism to neighboring nuclear spins. Improved understanding of the relationship between mechanism and structure should facilitate design of molecules with desirable magnitude and sign of the spin polarization generated as well as provide guidance for the magnetic field and temperature conditions where polarization may be accessed. The dominant mechanisms rely on an interplay of spin-spin coupling, magnetic anisotropy, orbital overlap, and sources of spin relaxation. 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 · 2024-07

To make decisions, people must rely on their understanding of the relevant environment: what are the causes and outcomes of the various forces at play. In other words, in many settings, including economic ones, people rely on subjective causal models (or narratives) to understand the world. Such models help agents organize and interpret information, allowing them to make forecasts about the future, and providing them with a way to evaluate counterfactuals. The main goal of this research is to take a first step towards understanding how economic agents come to adopt (possibly incorrect) models and how this depends on the information available to them. The researchers will approach this topic from two different perspectives. The first involves a series of experiments that aim to understand how people’s subjective models arise from patterns they identify in data. Some experiments will be conducted in an abstract setting, while others involve natural context. Natural context can trigger preconceptions about how different variables are associated with each other that may help or hinder people from correctly identifying actual patterns in a set of observations. The second approach aims to better understand whether news media plays a role in heterogeneous subjective models. The goal is to study whether different news outlets organize and explain the same outcomes using different causal models. A growing literature in economic theory studies ramifications of adopting possibly incorrect subjective models, referring to economic agents relying on such models as ‘misspecified.’ But, for the most part, the literature is silent on how a person comes to adopt a subjective model to begin with, how such a subjective model may depend on the setting, and how it may be shaped by the person’s experiences. In addition, it is an open question under what conditions people adopt subjective models that are consistent with the true data generating process. The goal of this research is to take a first step towards understanding how such misspecifications may arise and how they depend on features of the data-generating process. The researchers will approach the topic from two different perspectives. A first approach involves a series of laboratory experiments to understand how people extract patterns from their observations. The novel experimental design asks subjects to organize different sets of observations (data) with the goal of making predictions in similar situations. The experimental data will let the researchers understand whether the predictions subjects make in each environment are consistent with them using some model that posits specific statistical relationships between different variables. Complemented with ancillary non-choice data that emerges as a by-product of the experimental design, the results will provide insights into how people form models of the world by studying data and how they use these models to make predictions. Experiments will be conducted both with an abstract setting and with context. Understanding how people come to adopt (possibly incorrect) models and how this is impacted by the information available to them is important to determine in what situations they are more vulnerable to being manipulated. Furthermore, it can help us design policies that are effective in correcting beliefs and inducing optimal behavior. The second approach aims to better understand whether news media plays a role in shaping heterogeneous subjective models. The goal is to study whether different news outlets organize and explain the same outcomes using different causal models. To do so, the researchers will use an end-to-end trained Machine Learning pipeline that will take text (news articles) as input and identify the main causal statements advanced in this text as output. Documenting the heterogeneous causal models propagated by news outlets is important for understanding why voters with different political affiliation disagree on the optimal response to problems that are accepted by both sides. 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 · 2024-07

Project Summary Diverse neuronal types are specified into correct cell fates and connected with proper targets during circuit formation. Over the last decades, a number of cell surface molecules have also been identified that mediate axon guidance and connectivity. However, little is known about the coordination between neuronal specification and specific connectivity patterns, especially when two synaptic partners undergo two different modes of cell specification (stochastic vs. deterministic). The Drosophila color vision circuit is an appealing model to address this question mechanistically due to our deep knowledge of its development and neuronal connectivity, where Dr. Chen’s primary mentor, Dr. Claude Desplan, at New York University has been a leading expert in this field. In the fly retina, yellow (y) and pale (p) subtypes of color photoreceptors (R7 and R8) are stochastically specified, whereas their synaptic partners in the optic lobe are produced through highly deterministic programs. The first aim (K99) of this project is to characterize the y/p columnar stochastic circuits in the higher brain regions. Dr. Chen will perform EM connectomic analyses under the training of Dr. Michael Reiser to reconstruct the color vision circuit. Dr. Chen will also make highly cell-type-specific developmental driver lines for gaining genetic access to the cell types of interest. High-resolution transcriptomes for neurons downstream of either y or p pathway will be generated via Tango-seq under the mentorship of Dr. Chen’s collaborator, Dr. Justin Blau. The second aim (K99) of this project is to identify molecules required for synaptic partner matching. In collaboration with Dr. Graeme Mardon, Dr. Chen has accessed and used the single-cell RNA sequencing (scRNAseq) datasets of both developing retina and optic lobes to identify promising candidates that mediate synaptic connectivity of y/p neuronal subtypes. Dr. Chen will be trained by Dr. Robin Hiesinger to perform ex vivo live imaging of developing optic lobes to identify the functional role of these candidate molecules in synaptic partner matching. The third aim will be performed in Dr. Chen’s independent lab (R00) to study how the synaptic partner choices propagate to neurons further downstream by perturbating the cell fates of R7 and R8. Dr. Chen will compare whether a given neuron uses the same or different molecular codes for matching its pre- and post-synaptic partners. Successful completion of this proposal will uncover novel molecular mechanisms regulating synaptic pairing and probe the fundamental principles underlying the propagation of cell fate choices during circuit assembly. The principles identified here will be significant and applicable to other neuronal circuits facing similar developmental challenges, such as the olfactory system in rodents and color vision in humans. To achieve Dr. Chen’s career goal of becoming an independent scientist in a leading research university, Dr. Chen has assembled a great mentoring team, including Dr. Claude Desplan and Dr. Jeremy Dasen (co-mentor), as well as his advisory team to ensure a successful career and scientific progression during the funding period. .

NSF Awards · FY 2024 · 2024-07

Social media users sometimes encounter harmful content, such as sexual abuse, that they report to the social media platform. However, social media platforms sometimes fail to communicate with users about how these reports are handled. Information on how well social media platforms communicate with their users about the content they report is scarce. To address this problem, the research team is conducting interviews to understand what information people want to receive from social media platforms about the harmful content they report, developing systems to collect data about what information social media platforms share with their users about their reports, and comparing these data with reports published by social media platforms. Overall, this project aims to advance user welfare by shedding light on how social media platforms communicate with people about content they report. Transparency in platforms' decisions about user-reported harmful content not only helps users understand how and why their content is being handled but also lets researchers, community groups, and regulators quantify shortcomings in regulatory enforcement and hold platforms accountable. To ensure that the project is grounded in specific user needs, the investigators focus in particular on reporting of Image-Based Sexual Abuse (IBSA): technology-facilitated violence in which someone’s intimate images are shared without their consent. This project takes a data-driven approach to evaluating how effective existing platform transparency approaches are and to improving those approaches. The investigators are examining existing platform transparency efforts to understand their strengths and weaknesses from the perspectives of end users and researchers; developing methodologies to generate new transparency data through user-facing systems and novel use of platform APIs; and making use of new legal data access rights for researchers to obtain new types of transparency data from platforms. 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 · 2024-07

The International Nitrogen Network (iN-Net) will drive cross-cutting research to help countries meet ambitious policy goals of dramatically cutting nitrogen losses to the environment. Nitrogen pollution - fueled primarily by excess fertilizer and manure on agricultural land - is a major threat to the environment and human health. Once lost to the environment, nitrogen makes almost every environmental problem worse: from air and water pollution, to biodiversity loss, stratospheric ozone depletion and climate change. Policymakers have started to take notice: a growing number of national, regional and global efforts have coalesced around the goal of halving nitrogen losses to the environment, and countries will need to develop national action plans to make this goal a reality. Building effective plans requires the scientific community - in collaboration with a range of stakeholders from the public and private sector - to fill some fundamental scientific gaps: a lack of data in key areas; ineffective governance options; a fragmented understanding of the links between nitrogen and climate action; and weak communication between scientists and other communities. iN-Net will address these gaps by creating working groups of scientists and stakeholders to shape and drive research agendas on data, governance and nitrogen-climate interactions; establish a data platform that will enable countries to measure their progress and identify effective actions; and implement a workforce development program to train the existing and next generation of nitrogen scientists to be capable communicators and do policy-relevant research. It will cement US intellectual leadership on this important issue, while designing and implementing data, models, governance options and other approaches that will be critical for humanity to improve its complicated relationship with this essential resource and major pollutant. We propose to establish the International Nitrogen Network (iN-Net), an international network of networks, to address the fundamental gaps between research, policy and action that are currently impeding the development of national action plans to successfully operationalize the ambitious global goal of halving nitrogen (N) waste by 2030. These gaps include: inconsistent and incomplete data to establish a baseline and measure progress; a paucity of effective governance options to implement plan measures; and a fragmented understanding of the links between N and climate action. Cutting across all of these gaps is an inability to effectively communicate across scientific communities and stakeholder groups. Under the aegis of the the International Nitrogen Initiative (INI) and in collaboration with a National Science Foundation (NSF)-funded global center on N innovation as well as several regional and international scientific and science-policy networks, we will: 1) Create three transnational and transdisciplinary working groups with broad stakeholder participation to tackle the major gaps (data, governance, climate, and communication) for translating the halving N waste goal into national action plans; 2) Establish a collaborative data platform to be a ‘one-stop shop’ for N data and indicators for researchers, policymakers and other stakeholders to establish baselines and measure progress towards the implementation of national action plans; 3) Initiate several workforce development initiatives, including policy fellowships that will allow scientists and students to experience and contribute to global governance deliberations on N. In short, iN-Net will transform INI from a convener of the international N research and stakeholder community to a catalyst for novel research and policy approaches to help translate halve N waste targets into national action plans. 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 · 2024-06

How DNA is organized into chromosomes in human cells and how this complex folding controls gene expression fundamental to life defines one of the Grand Challenges of biology. Because chromosome folding occurs over a wide range of length and times scales, a sophisticated combination of experimental and computational approaches is required to unravel genome organization. In this collaborative project, a team from NYU (Tamar Schlick), Harvard (Anna Lappala and Jeannie Lee), and New Mexico Consortium (Karissa Sanbonmatsu) will combine expertise in molecular biology, genomics, computational biology, biophysics and biomolecular modeling in an integrated experimental and computational effort to address key mechanistic questions about X-chromosome structure and function. Inactivation influences X chromosome expression in females and has profound health implications. A better understanding of X-chromosome folding and gene expression will lead to new avenues for treating other diseases related to X-chromosome status. The research program will offer interdisciplinary and multidisciplinary training to young scientists in STEM fields, through hands on mentoring and participation in summer camps, promoting national needs in education and innovation in Science and Engineering. Public outreach will be further enhanced by communicating these scientific and societal advances in the form of a public museum exhibit. Advances in epigenomic methods have opened new avenues in chromosome folding studies. Yet translating such experimental information into three-dimensional gene structures is complex. The team will focus on X-chromosome inactivation (XCI), a balancing of X-linked gene expression between females and males, and will dissect this process at multiple scales – from nucleosomes to chromosomes. The team will integrate high-resolution genomics data with advanced spatial modeling to unravel XCI dynamics to interpret related gene function and activity. Namely, Lappala’s gene modeling techniques, Lee’s structure / function studies of the X-chromosome, Sanbonmatsu's chromosome dynamics simulations on the macroscale, and Schlick’s gene and chromatin modeling and simulations at the mesoscale will shed light on genome organization and function. Included are delineation of protein-mediated effects by cohesin and CTCF complexes on XCI and development of models of XCI at various spatial and temporal resolutions. The research will deepen our understanding of XCI and related mechanisms of chromatin organization and epigenetic regulation, offering new modeling and simulation tools for genes and chromosomes and advancing the fields of genomics and epigenetics. This project is jointly funded by the Molecular Biophysics Program in the Molecular and Cellular Biosciences Division of the Biological Sciences Directorate and the Established Program to Stimulate Competitive Research (EPSCoR). 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.