New York University

NIH Research Projects · FY 2025 · 2025-08

Confronted with the pressing challenges of cancer and other complex diseases, the pivotal role of heterogeneity learning becomes undeniably clear. This approach offers a fine-tuned lens, enabling us to decipher key disease characteristics, direct precise therapeutic strategies, and mitigate widening treatment disparities among diverse patient subgroups. Addressing this need, our proposal champions the development of cutting-edge statistical methods tailored for high-dimensional heterogeneity learning. Specifically, we intend to innovate in studying subgroups within genomic, transcriptomic, or connectomics data, even amidst potential data shifts and intricate multidimensional-array structures. Traditionally, unsupervised heterogeneity learning has sought to partition unlabeled heterogeneous data into distinct clusters, each expected to conform to a unique distribution. This commonly known “model-based heterogeneity learning” characterizes data as a mixture of distributions. However, the prevailing Gaussian presumption in this approach can be limiting and at times, off-mark. To rectify this, our project introduces a novel class of mixture models, a departure from conventional parametric models. This new class stands out as it refrains from setting strict distributional assumptions, meeting the growing demand for more adaptable heterogeneity learning tools. To validate the effectiveness of our techniques, comprehensive assessments will be carried out using The Gene Expression Omnibus Database, The Cancer Genome Atlas Database, and a study focused on intricate pediatric conditions. The success of this endeavor not only promises breakthroughs in research on life-threatening ailments but also sets the stage for groundbreaking statistical methods for a wider scope in neurosciences and biomedicine.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Debilitating post-treatment fatigue following stereotactic body radiotherapy (SBRT) to treat prostate cancer is a significant public health concern due to its negative impact on health-related quality of life, higher comorbidities, and shorter survival. To date, there are limited effective targeted fatigue therapies. Limited therapeutic options are due in part to inconsistent evidence on the individual and combined influence of social determinants of health (SDOH) risk factors such as dietary adversity (i.e., food insecurity, poor dietary quality), socioeconomic deprivation at the neighborhood level, and genetic factors that could ultimately influence biological pathways altering cancer-related fatigue (CRF) risk. Importantly, we recently showed associations between lower dietary quality and post-treatment CRF among food insecure prostate cancer individuals. One pivotal pathway that may link dietary adversity to CRF risk is the mammalian rapamycin complex 1 (mTOR/mTORC1) which is involved in autophagy to regulate cell growth and in energy homeostasis. Our recent work (NR020039) using next- generation RNA sequencing (RNAseq) from whole blood followed by gene and pathway ontology analyses identified novel significant associations between the expression of HAS1, PIK3R1, RASA4, SMAD1, TIRAP, and VEPH1– all related to mTORC1 – with CRF at 1 month after SBRT, suggesting a key role for the mTOR pathway in CRF. Our evidence highlights the importance of understanding if these risk factors individually or combined elevate the severity of CRF. The proposed research is guided by a model of SDOH, and dysregulation of the mTOR pathway in post-treatment fatigue. An experienced team of scientists will conduct the proposed project, employing rigorous methods using “-omic” approaches to investigate our central hypothesis, by leveraging our K23 NR020039, that genes involved in mTORC1 and related pathways are differentially expressed in CRF. We anticipate that dysregulation of mTOR-pathway genes, dietary adversity, and other SDOH will be enriched in men with prostate cancer who experience CRF. To attain our objective, in a new independent cohort (n=150) at pre, and 1 month after SBRT, we propose to: 1) validate dysregulation of 6 mTOR pathway genes in an independent cohort (Aim 1); and 2) determine the impact of dietary adversity, and other SDOH on post-treatment CRF (Aim 2a) and analyze the contribution of the 6 mTOR pathway genes and dietary adversity on post-treatment CRF (Aim 2b). At the end of this project, the proposed research will advance management strategies for cancer-related symptoms by providing proof of the principle of underlying factors that increase risk of fatigue and support further development and replication of serum biomarkers of fatigue severity and modifiable biologically relevant therapeutic targets to treat fatigue and improve outcomes in this population.

NSF Awards · FY 2025 · 2025-08

Urbanization is shaping the future of our world, with more than two-thirds of the global population projected to reside in cities by 2050. This significant challenge aligns with scientific innovations in embodied AI and smart cities, poised to revolutionize the way intelligent agents interact with the physical world and respond to the needs of varied and evolving urban communities. However, limitations in deployment range and the risks in real-world experiments hinder systematic developments and evaluations of emerging research in embodied AI. A promising pathway to address this gap is the creation of high-fidelity 4D digital twins of large-scale urban environments. This project supports research looking to develop DigitizedNYC, a realistic 4D digital twin of New York City that provides a shared platform for testing and comparing AI systems and smart city technologies. It helps researchers create repeatable experiments, making scientific studies more reliable and easier to build upon. This open and community-driven platform will promote reproducible science, support national infrastructure for embodied AI research, and advance fields such as autonomous mobility, urban planning, and accessibility. By integrating AR/VR tools, it will also provide immersive STEM education and outreach to students and professionals, contributing to a well-prepared scientific workforce. Many existing urban digital twins rely on aerial or indoor models that miss the complexity of real street-level environments, or on 2D videos that lack interactive capabilities. Creating accurate 4D digital twin models remains difficult due to visual challenges like dynamic lighting, weather, and moving objects, as well as technical issues such as sensor drift from long mapping trajectories. To fill this gap, this project performs research that provides a safe, realistic research cyberinfrastructure, accelerating the trustworthy development of embodied agents and smart city technologies. This project explores the following innovations: (1) Long-term multi-traversal mapping that will utilize consensus across multiple traversals to retain only consistent and permanent elements of the urban environment, while transient objects, such as pedestrians and vehicles, will be filtered out; (2) High-fidelity simulation will turn the large-scale 3D data into a 4D simulation that can be used for evaluation and even training of robot learning algorithms; (3) Science-driven cyberinfrastructure demonstration will show how DigitizedNYC serves as a city's important infrastructure that allows worldwide researchers and developers to easily test and fairly compare solutions for various embodied AI tasks, such as urban navigation. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Civil, Mechanical and Manufacturing Innovation within the Directorate for Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Titanium dioxide (TiO₂) is widely used as an opacifier and pigment in everyday products, including paints, coatings, cosmetics, and foods. However, the production of TiO₂ is expensive and faces significant supply chain challenges. This award brings researchers from US and UK together who seek to develop hollow polymer particles as sustainable replacements for TiO₂. By controlling the structure and surface chemistry of these polymer particles, their performance is expected to be enhanced to match or exceed the effectiveness of traditional TiO₂-based opacifiers. Successfully replacing TiO₂ with these advanced polymer particles is likely to improve supply chain resilience, promote public health by minimizing exposure to potentially harmful materials, and enhance economic prosperity through innovation. The outcomes of this award could benefit society broadly by promoting sustainable practices across multiple industries and by training the next generation of scientists and engineers. Research enabled by this award aims to fully replace titanium dioxide (TiO₂) opacifiers with hollow polymer particles in formulations such as paints and coatings. It is critical to optimize hollow particle architectures and their interactions with other ingredients in film-forming products to achieve comparable or superior opacity, thermal properties, and mechanical robustness relative to conventional TiO₂-based formulations. Specific objectives include synthesizing hollow polymer particles with controlled size, shell thickness, nanoporosity, and surface chemistry; integrating these particles into basic and industrially relevant coating formulations; and correlating particle features and distribution with final product performance. Experimental approaches are planned to utilize scalable emulsion-based synthetic methods, advanced microscopy, spectroscopy, and rheological techniques to systematically investigate particle dispersion, optical scattering, and coating properties. Collaborations with industry partners facilitate real-world application and commercialization through lifecycle assessments and practical formulation trials. This award looks to offer significant advances in polymer colloid science, complex fluids, and sustainable materials, paving the way for innovative, low-cost alternatives in multiple sectors. This award is made under the NSF-UKRI lead agency opportunity. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Diao of New York University is studying new ways to make medicines more efficiently and in a way that is better for the environment. Many medicines are made from small molecules whose 3D configurations are very important—they affect how well the medicine works and how long it stays in the body. Right now, most of these molecules are made using the conventional methods that create a lot of extra waste. A newer approach, called asymmetric catalysis, could make these medicines faster and with less waste, but it is limited by the availability of special tools called "chiral ligands." This project will focus on creating new chiral ligands that work with earth-abundant metals to make the process cleaner and more efficient. This research will also give students valuable experience in solving scientific problems, working in teams, and understanding how their work can help society. In addition, the project will include outreach activities like working with the New York Public Library to lead hands-on science experiments for children and creating a chemistry-themed video game to inspire interest in science, technology, engineering, and math (STEM). These efforts will help spark curiosity and encourage the next generation of scientists. With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Diao of New York University is studying new chiral nitrogen ligands to facilitate asymmetric base metal catalysis. Bidentate nitrogen ligands are effective in base metal catalysis for their strong coordination and redox activity, yet chiral nitrogen ligands remain limited. To address this challenge, this project will develop novel C1-symmetric bidentate nitrogen ligands—Imine-Oxazoline (ImOx) and Imine-Imidazole (ImIm)—derived from amino acids. The low symmetry of these ligands will enable independent optimization at each coordination site to enhance catalytic performance. Preliminary studies have established the synthesis of ImOx and demonstrated its ability to improve the enantioselectivity of palladium-catalyzed conjugate additions, showing a strong correlation between enantiomeric excess and steric effects at both donor sites. Building on these findings, research in this project will expand the ImOx ligand library, synthesize ImIm analogs, and apply these ligands in nickel-catalyzed asymmetric reductive conjugate addition reactions. The resulting pharmaceutically relevant products will support drug discovery efforts. Complementary organometallic studies will elucidate the structural and redox properties of ImOx, ImIm, and their metal complexes, thereby advancing ligand design principles for asymmetric catalysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

The analysis of materials from human societies offers insight into human technological practices and the fabric of innovation. This project examines the technological innovation, context, and significance of metal alloying technologies to better understand the effect of human decisions and environmental context and constraints on technological practices. In addition to training a graduate student, the study advances knowledge about critical human innovations and supports graduate student STEM training and K-12 science outreach activities. The project examines the provenance, alloying, working, and circulation of copper alloy objects, to better understand the factors influencing technological choices in the production sequence and the reciprocal relationship between people and materials. The investigator assesses societal practice through the collection and synthesis of compositional and microstructural data of metal objects from multiple sites, using a multimethodological approach (e.g., pXRF, lead isotope and ICP-MS, LA-ICP-MS, metallography, and SEM-EDS). The project reconstructs metal production and circulation practices and charts the extent of brass alloying temporally and spatially. The spectometric and chemical analytical methods align with the agency's priorities in the area of biotechnology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-08

Power consumption is one of the most fundamental constraints in the design of any wireless communication system. Recent trends in wireless technology and its applications have now made power consumption considerations particularly urgent. For example, new cellular systems in the millimeter wave (mmWave) bands offer massive data rates but have come at the cost of staggering power consumption due to the need to support a large number of antennas at a high bandwidth. At the same time, new low-power technologies can now operate in the milli-watts of power but remain limited in data rates. As new embedded systems are increasingly demanding high data rates for sensor and camera data and cloud connectivity, fundamentally novel approaches will be needed to develop high-speed, but energy-efficient wireless networks. This project aims to develop such energy efficient wireless systems through a combination of analog and digital circuits, processing algorithms and communications theory. This project seeks to develop a reliable, energy-efficient high-speed wireless communication system through hardware and algorithm codesign, which allows for very low power digital and radio frequency (RF) hardware with imperfections such as non-linearities and high noise to be compensated by innovative communication algorithms and enables the adaptation of power trade-offs across different components based on channel and network conditions. The work is composed of four interrelated thrusts: Thrust 1 develops new low-power and reconfigurable RF components along with fundamental capacity bounds and signaling methods. Thrust 2 then develops energy-efficient methods to achieve these bounds with co-design of digital hardware and communications algorithms. Thrust 3 extends the methods to multiple antenna systems through novel compact low-power arrays and beamforming processing. Thrust 4 validates the developed methods and techniques with fabricated circuits and a novel low-power emulation platform. 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 · 2025-08

PROJECT SUMMARY Astringency is a dry, puckering flavor sensation that is elicited by phenolic compounds, including tannins, in plant-based foods. The sensation of astringency can drive food choice and potentially impact whether or not we consume healthy, phenol-rich foods. Astringency has long been thought to be a tactile sensation, transduced by oral mechanoreceptors that sense roughness; however, there are gaps in knowledge regarding the mechanisms that underlie phenol transduction and astringent sensation. Multiple models of phenolic compound transduction have been proposed. Some rely on tannin-induced alterations in the biomechanical properties of saliva and the mucosal surface, resulting in enhanced surface roughness, which is then sensed by mechanosensory neurons in the oral mucosa. Other models implicate chemosensory mechanisms of astringency transduction, mediated either by oral mucosal cells that signal to other somatosensory neurons, or direct chemosensory transduction through somatosensory neurons. At this time, there is not a conclusive answer as to whether astringent flavor is chemosensory, mechanosensory, or a combination thereof. Our preliminary data suggest that phenolic compounds are transduced by a trigeminal mechanosensory neuron subpopulation that is tuned to detect movements. This activation occurs in the absence of oral movements, suggesting a chemosensory mode of transduction. Thus, we hypothesize that phenolic compounds directly activate a movement-detecting, tongue- innervating trigeminal mechanosensory neuron subpopulation through chemosensory mechanisms, and that this activation is necessary for astringent perception. In this proposal, we will use a combination of transgenic animals, pharmacology, and molecular methods to define the modes of astringency transduction (e.g., chemosensory or mechanosensory), identify the cellular compartments necessary for transduction and sensation, and determine the molecular signatures of astringent-responsive, tongue-innervating sensory neurons. We will first define the neuronal mechanisms of transduction of astringent compounds (Aim 1) using in vivo and ex vivo functional imaging. Then, we will identify the neuronal and cellular mediators necessary for aversion to astringent compounds using behavioral preference testing (Aim 2). Finally, we will establish the molecular signatures and receptor profiles of tongue-innervating sensory neurons using single cell RNA sequencing and computational analysis (Aim 3). Collectively, these studies will be among the first to systematically test the cellular mechanisms of phenolic compound transduction and sensation, with the long- term goal of understanding the contributions of oral somatosensory neurons to flavor construction.

NIH Research Projects · FY 2026 · 2025-08

A major goal of contemporary neuroscience is to understand how multiregional interactions instantiate computations for behavior and cognition. However, in complex cognitive tasks, there are many quantities and abstract relationships that must be computed, and it can be difficult to know which computations are specifically supported by the brain regions under study. This limits understanding of inter-area communication in support of behavior. We recently demonstrated a causal relationship between neural dynamics in the orbitofrontal cortex (OFC) of rats performing a temporal wagering task, and a precise behavioral computation - updating subjective beliefs about hidden states of the environment. We will leverage this finding to characterize how OFC interacts unidirectionally with the dorsal striatum (dStr) and bidirectionally with the basolateral amygdala (BLA) to support belief updating and context dependent decision making. We will perform simultaneous electrophysiological recordings from OFC and dStr or BLA and use novel statistical models of their joint population activity to characterize the distributed nature of their joint function. Our novel statistical modeling approach provides a description of the process by which these brain regions interact and communicate. Statistical data analysis will be complimented by multi-area recurrent neural network models (RNNs) that exhibit rat-like behavior in this task, and that will provide an opportunity for hypothesis generation and hypothesis testing in parallel with experiments. We will also causally manipulate OFC neurons that project to dStr and evaluate effects on behavior, and perform optogenetically-tagged recordings to characterize the responses of single neurons projecting to dStr. The results will provide a computational framework for determining how interactions between brain regions support computations for cognition. It will relate these dynamics to a precisely defined, core cognitive computation that we have shown causally requires the OFC: updating subjective beliefs about abstract, latent states of the environment based on outcomes. RELEVANCE (See instructions): Substance use disorders are characterized by aberrant value-based decision-making, and are thought to reflect dysfunction of cortico-basal ganglia circuitry. A better understanding of how these circuits support value-based decision-making could identify novel therapeutic targets for these disorders.

NIH Research Projects · FY 2026 · 2025-07

PROJECT SUMMARY Food insecurity, that is, the lack of consistent access to nutritious and affordable food, is associated with poor diet, increased cardiovascular disease (CVD) risk, and has a negative long-term impact on the economy through increased health care costs. CVDs and food insecurity disproportionately affect low-income families, which can be mostly attributed to social determinants of health, including poor access to healthy foods. A recent policy in the U.S. authorized the Supplemental Nutrition Assistance Program (SNAP) benefits to be used online to increase grocery access and promote healthy eating. Although online grocery shopping has been growing among populations of low-income, the selection of fruits, vegetables, and legumes (FVL), which are protective against CVD, is lower than in-store. Distrust of online hired shoppers’ choices, fear of losing money on unsatisfactory purchases, and impulse to unhealthy food purchases have been the major barriers to online healthy food selection. Thus, there is a need for intervention packages that are effective, economical, and easily scalable into policies that address CVD-related outcomes and ensure everyone has the opportunity to achieve optimal health. The proposed work will use a highly efficient methodological approach, the Multiphase Optimization Strategy (MOST), to test three experimental components aimed at barriers to online healthy food selection, called OPT-FRESH. This approach addresses weaknesses in prior studies, which cannot determine which elements of multicomponent interventions meaningfully improve outcomes. 360 families with children living in lowincome urban communities of NYC will be randomized to receive some combination of the three experimental components for 12 weeks: 1) weekly text messages to improve the trust in online grocery services (off/on); 2) weekly match-up to $10 as a financial incentive for purchasing FVL online targeting loss aversion (off/on); 3) weekly instant shoppable grocery lists of culturally tailored meal suggestions using SNAP recipes targeted at impulse purchases (off/on). Delivery fee waiver and a tutorial video addressing digital literacy will be constant components to promote access in program participation. Aim 1 will determine the combination(s) of the three experimental components that improve overall household FVL purchase and food insecurity (primary outcomes) and FVL intake of children (secondary outcome). Aim 2 will identify the optimized intervention that balances component(s) that are affordable and scalable (high adoption, implementation, maintenance) that still produce meaningful effects on the outcomes, using decision analysis for intervention value efficiency. Aim 3 will determine the mechanistic effects of the three intervention components on the outcomes using factorial mediation analysis. Working with community-based organizations and nutrition and hunger relief programs in NYC, a grocery delivery service, and a team with unparalleled expertise in experimental trials, policy, and MOST, we will implement optimized, affordable, and scalable intervention strategies to improve neighborhood food access and ultimately CVD outcomes in low-income families.

NSF Awards · FY 2025 · 2025-07

Theoretical Computer Science (TCS) is the study of the power and limitations of computing. The study has two complementary aspects: algorithm design, which aims at designing fast, efficient algorithms to solve computational problems, and computational complexity, which aims at rigorously showing that for some problems, no efficient algorithm exists. The famous P versus NP conjecture posits that a class of problems, known as NP-hard problems, do not have efficient algorithms. These problems, nevertheless, do arise in a wide range of practical applications and do need to be solved somehow. One way to cope with their computational hardness is to seek approximate solutions in an efficient manner, with guarantees on the quality of approximation. This has been an intensely studied topic, leading to efficient approximation algorithms for a large variety of problems and subsequent applications in theory as well as in practice. It turns out however that there are limitations on the quality of approximation that can be achieved efficiently and study of these limitations is also intensely studied. The current project aims to study the approximation question for a natural sub-class of NP-hard problems known as Constraint Satisfaction Problems. In addition to designing approximation algorithms for them and showing their limitations, the project aims at developing required mathematical tools and at answering additional long-standing questions in computational complexity and discrete mathematics. Hardness of approximation refers to the phenomenon that for several NP-hard problems, even computing approximate solutions remains an NP-hard problem. Starting with the foundational Probabilistically Checkable Proofs (PCP) Theorem in the early 1990s, there have been numerous influential results in this area. One outstanding question is to understand the approximability of Constraint Satisfaction Problems (CSPs) on satisfiable instances. That is, given a satisfiable instance of a CSP (such as 3SAT), what is the maximum fraction of constraints that can be satisfied efficiently? It is well-known that 3SAT has a sharp threshold of 7/8, meaning, there is an efficient algorithm that, on a satisfiable instance of 3SAT, finds an assignment that satisfies 7/8 fraction of the constraints and doing strictly better than this threshold is NP-hard. The project aims to characterize such a sharp threshold for every satisfiable CSP and develop required mathematical tools which would have additional applications. The investigator and his research team already have substantial results in this direction, with applications to additive combinatorics and computational complexity. The investigator foresees that the work opens up several promising research directions, morphing into a broad, long-term, impactful program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

The rapid growth of online video content has created new opportunities for learning, communication, and civic engagement. However, current accessibility technologies leave many people, including deaf and hard-of-hearing (DHH) individuals and older adults, with incomplete access to this important information resource. While existing automated audio captioning technology is frequently used to transcribe the audio in online video, the focus is on spoken words and ignores environmental sounds, music, and speaking style. These things often carry important information, from the subtle audio cues that signal danger in safety training videos to the environmental sounds that establish setting and mood in educational documentaries. This project will develop adaptive artificial intelligence systems that can determine which of these non-speech sounds are important for understanding video content and present them in ways tailored to individual viewer needs and preferences. The research tackles the complex challenge of translating rich hearing experiences into understandable formats while respecting the different ways that individuals prefer to receive information. By creating tools that make non-speech sounds accessible in digital media, this project ensures that all citizens can participate fully in digital education, entertainment, and civic life. This project consists of a comprehensive agenda that combines human-computer interaction, machine learning and accessibility research. First, through user research with content creators and viewers, the project will investigate: "what non-speech sounds should be captioned?", "why should they be captioned?", and "how should they be captioned?" Results will inform design guidelines for tools to write and display captions. Second, the project will develop captioning datasets, including a large dataset of videos annotated for the needs of viewers. These datasets will further our understanding of the complex relationships that influence what should be captioned and how. Third, the project will develop a steerable and adaptive machine learning framework using multiple types of data (from our datasets) for audio captioning. In this framework, sound events will be: densely captioned with cues for meaning and sound; prioritized and decoded into text and visuals to communicate their meaning; adapted to the needs and preferences of viewers. Viewer needs and preferences will be discovered using a co-design approach with stakeholders. This project will create publicly available tools, guidelines, datasets, and machine learning frameworks to improve learning, communication, and civic engagement for millions of people who are DHH or experience decline in hearing capabilities. 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 · 2025-07

PROJECT SUMMARY Dental caries is the world’s most prevalent noncommunicative disease and negatively impacts child development. Both the NIDCR Oral Health in America report and the World Health Organizations’ Global Oral Health Status report call for improved access to and delivery of effective preventive oral care across the lifespan. School-based caries prevention can increase access to critical care for underserved children, reducing caries risk and mitigating its severe health and socioemotional consequences. The CariedAway study was a longitudinal pragmatic randomized trial of novel approaches to prevent and control dental caries in a school-based program, with a particular focus on low-income children. Previous findings from CariedAway demonstrated that (1) silver diamine fluoride (SDF) is equivalent to dental sealants and atraumatic restorations in the arrest and prevention of dental caries; (2) SDF can be effectively provided by school nurses, who are comparable to dental hygienists; and (3) treatment with SDF for caries prevention and control resulted in improved oral health-related quality of life. These findings support the large-scale utilization of SDF for school- based caries prevention, especially in severely under-resourced areas. In this R03 proposal we will conduct secondary analysis of the CariedAway data in order to support the wider implementation of silver diamine fluoride into school-based caries prevention. We will first explore the longitudinal arrest capabilities of school- based SDF in greater detail, including the additive effect of repeat application, the effect of posterior application on anterior caries incidence, and the transition probabilities between sound, carious, and arrested states. Second, we will estimate the surface-level risks of dental caries following treatment with SDF, modified by the severity of baseline disease. In this analysis, we will account for interval-censoring that occurs in school-based care using semi-parametric G-estimation. Finally, we will estimate the overall treatment time required for SDF in a school-based model and compare it to the time required for similar application of dental sealants and atraumatic restorations. The proposing investigators possess relevant, complementary expertise to ensure the success of this project: the PI is an epidemiologist and education researcher focusing on school-based care and was a Principal Investigator of the CariedAway project. The Co-I is a public health dentist with expertise in dissemination and implementation research, and was previously the Director and Supervising Dentist of the CariedAway project.

NSF Awards · FY 2025 · 2025-07

This project aims to study a variety of problems addressing fundamental features of the classical three-dimensional Ising model and its (2+1)-dimensional Solid-On-Solid (SOS) approximation at low temperature, which are central to understanding crystal growth and formation. The main focus will be on longstanding conjectures concerning the fluctuations of these surfaces and the scaling limits of the models when positioned on a slope and on a hard wall. This project also provides research opportunities for graduate students. The first research direction concerns the 3D Ising model and its (2+1)D SOS approximation at low temperature, with boundary conditions that force these random surfaces to have a positive slope. It is widely believed that, above a slope, the low temperature rigidity of these surfaces will destabilize, and their scaling limits should be a Gaussian Free Field (GFF). Little progress has been made on this problem until a recent successful study of a variant of the SOS model. The project outlines a program building on this recent progress, aiming to establish a GFF limit for the true SOS model and, thereafter, 3D Ising. The second research direction focuses on the 3D Ising and (2+1)D SOS surfaces above a hard flat wall. The plan is to solve key questions, addressing the typical height and shape of the Ising surface in that setting, and the fluctuations of the SOS level lines. The third research direction aims to study the SOS model above a hard wall (as above) but with an added pinning/layering potential, competing with entropic repulsion effects. The plan is to study the surface shape, dynamical phase transitions, and metastability at and near the associated critical points. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project explores questions at the intersection of combinatorics and probability, with connections to other fields. A significant focus is on gaining deeper insights into random discrete structures, particularly analyzing the locations of phase transitions (e.g., the molecular changes when water freezes into ice). These are important phenomena in various areas such as statistical physics, theoretical computer science, and network science, as they define the boundary between two phases with distinct properties. Recent advancements in this field have been groundbreaking, and the PI has been committed to advancing the innovative tools developed in recent years. The project will involve both graduate and undergraduate students in research. This project addresses two areas of research. The first focuses on the threshold phenomena of random discrete structures. The primary objectives of the project in this topic are to prove the ``Second" Kahn-Kalai Conjecture, which was the original motivation for the Kahn-Kalai Conjecture, and to prove a conjecture of Talagrand that concerns the suprema of certain classes of stochastic processes. To tackle these challenges, the PI aims to test the strengths and limitations of the methods developed in recent breakthrough results in this field, including some that the PI has contributed to. The second area investigates asymptotic enumeration problems on expander graphs. Various techniques - including entropy methods from information theory, container methods from extremal and probabilistic combinatorics, the cluster expansion method from statistical physics, and isoperimetric inequalities from discrete analysis - are expected to be exploited and improved to tackle the problems here. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

Designing and building mechanically robust, multi-functional materials is a challenging problem in engineering. However, naturally occurring biological materials typically perform multiple functions and are robust to environmental disturbances throughout their development. In contrast to most human-engineered materials, biological materials are often formed through self-assembly, a process that occurs when large-scale emergent structures form not from overarching designs, but instead from physical interactions between cells and other structures. One of the most important organs in nature is the plant leaf, which is the site of almost all terrestrial carbon fixation globally. Despite being seemingly planar, leaves are three-dimensional organs composed of multiple, porous tissues that develop from tightly compacted, undifferentiated cells. This award supports research to create a three-dimensional model of leaf development to recapitulate the structural diversity among real leaves and test how this structural variation influences leaf performance. In so doing, this project will create a "virtual leaf" platform for future studies of leaf function and advance the development of self-assembling, biomimetic materials, as well as mentoring and educating high school, undergraduate, and graduate students. Building stable, porous materials with tunable and targeted properties through self-assembly has the potential to transform material science and engineering. This project uses the plant leaf as a model of self-assembly because it is composed of multiple tissues organized in three dimensions (3D) that vary in properties ranging from completely confluent to highly porous, even though each of these tissues develop in unison from undifferentiated, identical cells. This research will (1) develop new image analysis methods to characterize the 3D cell and tissue structural diversity of leaves to guide modeling, (2) develop a 3D computational model of leaf development incorporating all major leaf tissues, and (3) test how structural variation at the cell and tissue levels influence the mechanical and functional properties of leaves. Together these aims will illuminate approaches for engineering multifunctional materials with tunable properties and structural heterogeneity. 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 · 2025-07

PROJECT SUMMARY Hypothalamic AgRP hunger neurons and POMC satiety neurons use neuropeptides AgRP, NPY, and αMSH to regulate food intake, energy expenditure, and body weight. One important downstream target is the satiety- promoting MC4R receptor-expressing neurons in the paraventricular nucleus of the hypothalamus (PVHMC4R), which receive converging peptidergic signals from AgRP and POMC neurons. However, exactly how PVHMC4R neurons interpret and integrate these peptide signals remains unclear, especially regarding the involvement of the downstream second messenger cAMP. The central unknowns can be summarized into three questions: (i) How do hunger and satiety peptides alter cAMP in PVHMC4R neurons? (ii) How does cAMP regulate the spiking of PVHMC4R neurons? (iii) Where in a PVHMC4R neuron is cAMP signaling most influential in regulating PVHMC4R activity? The main barrier to answering these questions is the lack of tools to measure and manipulate cAMP in PVHMC4R neurons. I have recently overcome this barrier by helping develop and apply a molecular and optical toolset to measure, make, and degrade cAMP during behaviors. The studies and career development activity in this K99/R00 proposal are designed to provide me with the necessary training to initiate an independent research program that applies the new cAMP tools to understand peptide signaling in PVHMC4R neurons. I hypothesize that coordinated, opposing changes in AgRP and POMC neuron activity regulate spiking in PVHMC4R neurons via competing actions on cAMP signaling. To test this hypothesis, I propose the following aims: Aim 1: To image cAMP dynamics in PVHMC4R neurons with the optical sensor cADDis during experimentally induced and feeding-induced peptide release, and to compare cAMP signaling between fasted and fed states. Aim 2: To measure how directly increasing cAMP (using a new optogenetic tool, biPAC) and degrading cAMP (using an engineered phosphodiesterase, PDE4D3-Cat) changes plasticity and spiking of PVHMC4R neurons. Aim 3: To engineer and use subcellularly targeted cADDis, biPAC, PDE4D3-Cat in order to understand local cAMP signaling in PVHMC4R neurons. The proposed research will be mentored by Drs. Mark Andermann and Bradford Lowell, who will also provide guidance on becoming an independent researcher. I will learn new techniques in intraventricular drug delivery, whole-cell patch-clamp, and measurements of energy expenditure from my mentors as well as Drs. Alex Banks, Maria Lehtinen, and Joseph Majzoub. I will receive training in protein engineering from Drs. Shiqiang Gao, Georg Nagel, Bernardo Sabatini, and Gary Yellen. I will acquire new skills from the above experts and from courses and scientific meetings, combined with my existing expertise in cAMP research. This will enable me to understand when, where, and how cAMP is used to titrate PVHMC4R neurons activity. These results will fill an important knowledge gap in developing and understanding treatments for obesity.

NIH Research Projects · FY 2025 · 2025-07

Project Summary This proposal requests funds to support predoctoral trainees as part of the Chemical Biology Interface Training Program (CBITP) at New York University (NYU). Our program will catalyze interdisciplinary education at the interface of chemistry and biology with a focus on two biomedical themes: Chemical Cancer Biology and Chemical Microbiology. This training program will have participation from four basic science departments at NYU: Department of Biology, Department of Chemistry, Center for Neural Science, and NYU Pain Center. All programs are under the umbrella of NYU Graduate School of Arts & Science, enabling low barriers to joint students and shared facilities. Participating faculty mentors have research interests in infectious disease, neuroscience, and cancer chemical biology, with specific expertise spanning developmental biology, genomics and proteomics, cell-signaling, organic chemistry, bioinorganic chemistry, enzymology, computation, bioinformatics, neuroscience, and structural biology. Trainees are educated in the principles and techniques of both chemistry and biology and take an interdisciplinary set of core courses to facilitate this knowledge. The unique aspects of this program are that the program stresses joint mentors from different scientific disciplines to encourage trainees to become proficient in chemical approaches applied to biological complexity as well as approaches to science from different perspectives. The course work emphasizes rigor and reproducibility in scientific research and scientific communication. All students will convert their key experimental methods into a scientific video similar to those in the Journal of Visualized Experiments (JOVE) to be presented at the monthly seminars or annual retreat. All trainees and mentors participate in the monthly Seminars in Chemical Biology series as part of the NYU Chemical Biology Consortium, and the annual symposium and poster session. Trainees will participate in their home graduate program research colloquia and student clubs in order to broaden their knowledge and networking opportunities beyond the training program. In Years 4 or 5 of graduate school, trainees will have an opportunity to pursue an internship at a pharmaceutical company or entrepreneurial office to gain perspectives on careers outside academia. The number of mentors participating in the CBI Training Program is 24 and four funded positions are requested in each year.

NIH Research Projects · FY 2026 · 2025-07

PROJECT ABSTRACT Significant advances in global health have been achieved in recent decades. Yet, serious variances in health outcomes persist, especially among children, adolescents and their adult caregivers. Sub-Saharan Africa (SSA) is one of the regions disproportionately burdened by multiple health threats, including endemic CDs; emerging and re-emerging infectious diseases; increasing incidence of NCDs, and a set of exacerbating factors that have contributed to poor public health and increased overall disease burden affecting children, adolescents and their adult caregivers. Similar trends are documented in several other LMICs, including countries in Asia and Eastern Europe. In light of widespread health challenges and gaps in the translation and uptake of scientific evidence in real-world settings in LMICs, dissemination and implementation science can advance timely and context- specific public health solutions. Moreover, significant methodological advances in data science can create new opportunities to more accurately identify sensitive populations, better understand patterns and mechanisms of health burdens, and allow for more in-depth analysis of implementation gaps and imbalances in healthcare systems and across populations in LMICs. The proposed research training program, entitled “ACHIEVE: Addressing the Research Capacity Gap in Global Child, Adolescent & Family Health Utilizing Implementation and Data Sciences”, focuses on increasing dissemination and implementation science and data science capacity to address global health issues affecting children, adolescents and their adult caregivers. The program addresses the following specific aims: Aim 1: To provide a research training program to five cohorts (~50 trainees) of health care professionals and post-doctoral trainees from the U.S., and post-professional degree graduates from SSA that equips trainees with dissemination and implementation and data science research skills and knowledge through experiential learning, mentoring, “hands-on” immersion in global health implementation and data science research and methodologies, individualized consultation, goal setting and monitoring and web based support across time; Aim 2: Bring together an inter professional network of committed mentors from the global north and the global south to promote bi-directional learning and collaboration and ensure quality training for promising new investigators committed to applying dissemination and implementation science and data science research methods to address health gaps impacting children, adolescents, and their families in low-resource settings; Aim 3: To examine the short-term and longitudinal outcomes of the ACHIEVE training program; and Aim 4. Delineate key factors that underlie successful mentorship and training of new investigators– with potential implications for new investigators who are focused on dissemination and implementation science and data science research that seek to address health gaps impacting children, adolescents, and their adult caregivers. The four U.S. universities have each committed matching funds totaling $600,000 to support the ACHIEVE program.

NSF Awards · FY 2025 · 2025-07

This project focuses on the design of secure account management tools and frameworks, advancing the science of digital account security and fostering safer digital ecosystems for people who face targeted attacks. Online accounts increasingly play a central role in people’s wellbeing, acting as the gateway to email, finances, social networks, geographic locations, and even domestic home environments. However, a growing amount of evidence suggests that people who face heightened threats to their digital safety are often targeted by abusive adversaries; adversaries who bypass trusted authentication mechanisms for unauthorized access to victim accounts, enabling further harassment, surveillance, and control. The research agenda has four aims. The first will lead to a better understanding of experiences of targets such attacks, support professionals, and abusive adversaries through qualitative, stakeholder investigations. The second will complement this knowledge by cataloguing existing management tools and emerging authentication mechanisms as provided by online services via new forms of measurement studies and new framework for analyzing the abusability of account management tools. The third will offer new designs of account management tools to improve detection and investigation into illicit accesses, refining them via design provocation studies with relevant stakeholders. The fourth will create new empirically-grounded interventions for the targets to restore their digital safety, and the perpetrators of attacks to deter technology abuse at its source. The intellectual contributions of the work include: 1) new analytical frameworks for assessing illicit client-side access to accounts; 2) new frameworks for reasoning about UI-bound attacks and the abusability of tools; 3) new design patterns for account security management that can be applied widely; and 4) new direct interventions with both the targets of such attacks and abusive adversaries. 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 · 2025-06

PROJECT SUMMARY Vaccines play an essential role in controlling and preventing infectious diseases in humans. The efficacy of vaccine depends on the extent to which the vaccine enhances the adaptive immune response in the body, which remembers and eliminates the foreign invader. However, the lack of reliable preclinical model for human adaptive immunity modeling and vaccine assessment have frequently led to vaccine products failing in clinical trials. Interspecies differences between current animal models and humans hinder accurate replication of human immune responses for vaccine assessment. Adaptive immune response to vaccine is mainly initiated in lymph nodes (LNs), which serve as vital hubs for various types of adaptive immune-related lymphocytes. LNs are highly structured secondary lymphoid organs with essential functions reliant on the unique spatial arrangement of lymphocytes and stromal cells as well as chemokines that drive the signaling cascades underlying the adaptive immune response. After vaccination, vaccines drain to the LNs, where antigen- presenting cells recognize and phagocytize vaccine antigens to induce T cell and naïve B cell activation and differentiation for antibody secretion that sustain long-term immune responses. Changes in the LN niche such as depletion of CD4+ T cells, reduced germinal centers, and LN extracellular matrix (ECM) fibrosis due to genetic variation, disease, aging, or chronic inflammation can lead to delayed immune responses affecting vaccine efficacy across populations. Developing models of LNs that accurately replicate their intact immune niche, describing the dynamics and organization of LNs, is a significant challenge in human adaptive immunity research. To date, no reliable in vitro model of LNs has been able to simulate the complete dynamic process of adaptive immunity for vaccine assessment. In this work we aim to develop a human “Lymph node-on-a-Chip” microphysiological system with microanatomic organization of key functional compartments (paracortex, follicle, subcapsular sinus, lymphatic and blood vessels) with full spectrum of LN cell types (dendritic cells, T cells, B cells, fibroblasts) and continuous lymph fluid perfusion to replicate the in vivo physiology of human LN microenvironment. These underscores the pressing importance of devising more clinically relevant vaccine testing models. The human Lymph node-on-a-Chip will serve as a unique precision medicine platform that provides a more accurate representation of adaptive immune responses than traditional animal models for vaccine effectiveness evaluation. This technology with a novel “clinical trials on a chip” protocol allows an accurate assessment of vaccines and adjuvants against emerging infectious diseases, thus aids in optimizing vaccination strategies for optimal protection for different populations.

NIH Research Projects · FY 2026 · 2025-06

PROJECT SUMMARY Artificially controlling neural activity in real-time is a central challenge in neuroscience and neuroengineering, with significant implications for understanding brain function and developing treatments for neurological and psychiatric disorders. Despite advances in high-precision neural perturbation technologies, a critical gap exists in predicting and shaping the effects of these perturbations on neural population activity and behavior. Our proposed research addresses this gap by developing a data-driven framework for real-time neural control, integrating theoretical models, empirical analyses, and advanced stimulation techniques. Among circuit perturbation methods, the effects of electrical stimulation remain particularly difficult to predict. However, this technique is widely used in clinical treatments of neurological and psychiatric disorders, including Parkinson’s disease, essential tremor, mood disorders, and obsessive-compulsive disorder. To maximize translational impact and showcase the strengths of our theoretical framework, we focus on multi-electrode patterned microstimulation sequences for circuit perturbations. In Aim 1, we investigate the spatial and temporal summation rules governing multi-electrode microstimulation in monkey prefrontal cortex. By delivering spatiotemporally patterned stimulation and measuring population responses, we aim to uncover the principles of nonlinear summation and interaction effects, such as whether responses are stronger for spatially or temporally proximal stimulations and for stimulation of high-impact “hub” electrodes. We also examine trial-to- trial variability in stimulation responses as a function of network state, offering insights into how pre-existing activity modulates stimulation effects. In Aim 2, we introduce a novel data-driven algorithm called Input/Output Control (I/O-Ctrl) that predicts optimal stimulation patterns to evoke specific neural activity states. This approach requires no prior knowledge of circuit dynamics or connectomes, instead relying on short training sequences of stimulation-response data. By applying I/O-Ctrl to prefrontal circuits, we aim to develop a generalizable framework for targeted neural modulation. In Aim 3, we extend these insights to behavioral control, exploring how neural perturbations in prefrontal cortical circuits influence decision-making in primates. Through closed-loop adaptive stimulation, we aim to identify neural mechanisms that bias choices and examine the causal relationships between population activity patterns and behavior. The proposed work is transformative, addressing key challenges in understanding and controlling neural circuits. By combining computational modeling, multi-electrode stimulation methods, and real-time neural control frameworks, this research has the potential to advance brain-computer interfaces, inform treatments for motor and cognitive disorders, and uncover fundamental principles of brain function. These efforts will significantly enhance our ability to manipulate and interpret neural circuits, with broad implications for neuroscience, medicine, and artificial intelligence.

NIH Research Projects · FY 2025 · 2025-06

PROJECT SUMMARY Artificially controlling neural activity in real-time is a central challenge in neuroscience and neuroengineering, with significant implications for understanding brain function and developing treatments for neurological and psychiatric disorders. Despite advances in high-precision neural perturbation technologies, a critical gap exists in predicting and shaping the effects of these perturbations on neural population activity and behavior. Our proposed research addresses this gap by developing a data-driven framework for real-time neural control, integrating theoretical models, empirical analyses, and advanced stimulation techniques. Among circuit perturbation methods, the effects of electrical stimulation remain particularly difficult to predict. However, this technique is widely used in clinical treatments of neurological and psychiatric disorders, including Parkinson’s disease, essential tremor, mood disorders, and obsessive-compulsive disorder. To maximize translational impact and showcase the strengths of our theoretical framework, we focus on multi-electrode patterned microstimulation sequences for circuit perturbations. In Aim 1, we investigate the spatial and temporal summation rules governing multi-electrode microstimulation in monkey prefrontal cortex. By delivering spatiotemporally patterned stimulation and measuring population responses, we aim to uncover the principles of nonlinear summation and interaction effects, such as whether responses are stronger for spatially or temporally proximal stimulations and for stimulation of high-impact “hub” electrodes. We also examine trial-to- trial variability in stimulation responses as a function of network state, offering insights into how pre-existing activity modulates stimulation effects. In Aim 2, we introduce a novel data-driven algorithm called Input/Output Control (I/O-Ctrl) that predicts optimal stimulation patterns to evoke specific neural activity states. This approach requires no prior knowledge of circuit dynamics or connectomes, instead relying on short training sequences of stimulation-response data. By applying I/O-Ctrl to prefrontal circuits, we aim to develop a generalizable framework for targeted neural modulation. In Aim 3, we extend these insights to behavioral control, exploring how neural perturbations in prefrontal cortical circuits influence decision-making in primates. Through closed-loop adaptive stimulation, we aim to identify neural mechanisms that bias choices and examine the causal relationships between population activity patterns and behavior. The proposed work is transformative, addressing key challenges in understanding and controlling neural circuits. By combining computational modeling, multi-electrode stimulation methods, and real-time neural control frameworks, this research has the potential to advance brain-computer interfaces, inform treatments for motor and cognitive disorders, and uncover fundamental principles of brain function. These efforts will significantly enhance our ability to manipulate and interpret neural circuits, with broad implications for neuroscience, medicine, and artificial intelligence.

NSF Awards · FY 2025 · 2025-06

This I-Corps project is based on the development of flood sensor technology that can be used to measure street-level flooding and provide actionable data to municipalities in real time. Flooding is a significant problem that requires data-driven interventions to mitigate its impact on urban areas. However, quantitative data on flood frequency, depth, and duration that are required for real-time response as well as the design of flood mitigation programs are not available in most municipalities across the United States. This flood sensor technology was developed to address this data gap and support the design of interventions for flood resilience, emergency planning, and response. The technology has the potential for large-scale societal impact, by providing hyper-local, real-time flood data to city agencies to optimize emergency response and infrastructure planning, ultimately mitigating the risks of property damage and loss of life. In addition, the technology is designed to make flood data accessible to residents, enhancing public awareness and preparedness related to floods. A pilot test of the system is currently underway in New York City, and the model could be scaled to flood-prone cities globally. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of an urban flood monitoring system. This is a sensor-based technology with supporting network infrastructure and real-time data processing pipelines that ensure the timely delivery of actionable flood event data. The solution is designed specifically for measuring flood frequency, depth, and duration in complex urban environments and includes: low-cost flood sensors that measure street-level flood depth at high resolution without requiring existing power or network infrastructure; cellular network integration, for transmission of data from sensors to servers in real time; artificial intelligence (AI)-driven data pipelines to process raw telemetry into actionable insights for stakeholders; and a suite of data services, including real-time flood data dashboards with time-series and map-based visualizations. A current pilot project consisting of over 250 flood sensors deployed in New York City has demonstrated an interest in the use of the technology by city agencies and residents. Results show that city agencies, including those responsible for emergency services, flood mitigation, transportation, and housing, require flood data for public safety and infrastructure design and planning applications. This model system may offer a replicable framework for other flood-prone cities to combat challenges posed by flooding. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry Professor Marvin Parasram of New York University is studying the development of spatially controlled C–H functionalization reactions empowered by photochromic directing groups. The ability to functionalize strong and prevalent C–H bonds is a contemporary challenge in synthetic chemistry. Scientific advances in the area of transition metal (TM) catalyzed directed C–H functionalization reactions are limited to mono-functionalization using difficult-to-remove directing groups (DG). Moreover, the planarity of the employed DGs does not allow for spatial control, and functionalization is limited to predisposed C–H bonds based on favorable organometallic cycles. The research team will investigate the merger of photochromism and TM–catalyzed C–H functionalization to empower directional control of DGs to enable last-stage C–H difunctionalization at different spatial sites of the molecule. The ability to utilize photochromic DGs for positional controlled C–H functionalization has the potential to expedite the synthesis of important synthetic cores in a highly selective and diverse manner, thereby solving a prominent challenge in synthesis. The education and outreach activities will focus on increasing STEM interest through the development of research-based STEM laboratory classes for undergraduate and high-school students. For graduate students, a non-technical seminar focusing on a speaker’s career journey toward achieving their scientific career goals will be carried out. Lastly, a social/networking triannual symposium with the organic divisions of local New York City universities will be created to promote scientific interactions with graduate students and postdocs. In this work, Dr. Parasram leverages the interplay between photochromism and TM-catalyzed directed C–H functionalization to enable spatial selective C–H difunctionalization of organic molecules. The PI seeks to overcome the inherent limitations of state-of-the-art methodologies by 1) employing easy-to-remove photochromic DGs, 2) allowing directionality control of the directing group using different wavelengths of light, and 3) employing unsymmetrical electrophiles to generate unsymmetrical difunctionalized products. Objective 1 explores the use of innate functional groups as photoswitchable DGs to facilitate two separate and spatially different C–H functionalization events, allowing for the multivectorization of organic motifs in a highly selective and efficient manner. Methods to carry out this approach asymmetrically using chiral ligands, and applications to access molecular scaffolds of biological interest including natural products will be explored. Objective 2 investigates the use of designer photochromic DGs for the difunctionalization of heterocyclic motifs. The directionality control of the photochromic DGs can enable the functionalization of unconventional C–H sites on heterocycles, thus, solving a longstanding problem in the 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.