Grants

Woodside on the Move, Inc.

Community Housing Preservation Strategies

United Jewish Council of the East Side, Inc.

Community Housing Preservation Strategies

West Side Federation for Senior and Supportive Housing, Inc.

Community Housing Preservation Strategies

West Bronx Housing and Neighborhood Resource Center, Inc.

Community Housing Preservation Strategies

City of Richmond

craftcanna

NSF

Flash memory systems are currently among the leading non-volatile memory technologies. To increase storage density, each memory cell in these systems is programmed to hold multiple bits . Errors in these systems tend to be of limited magnitude. This project aims to design efficient codes capable of correcting such limited magnitude errors, thereby enhancing the reliability of these systems. Additionally, in communication and magnetic recording systems, synchronization errors can occur in two forms. The first type is a deletion error, where a transmitted symbol is not received. The second type is an insertion error, where an unexpected spurious symbol is received. The propagation of these errors can greatly reduce the performance of these systems. This project plans to design efficient codes capable of correcting these types of errors, thereby improving the overall performance and robustness of these systems. The proposed research will also serve to advance graduate education through graduate research assistantships and undergraduate education via the Research Experience for Undergraduates (REU) program. Involving students in the proposed research will give them valuable experience transferable to many industry and career paths. At present, most known codes are designed using the power sums of index sets over a Galois field GF(q). This project proposes a unified approach to designing several new families of efficient error-correcting codes based on the theory of Elementary Symmetric Functions (ESF). The goal is to develop efficient linear and cyclic codes over Zm (integers modulo m) for both L1 and Lee distances using the ESF framework. Additionally, the project will explore the design of insertion/deletion error-correcting codes grounded in ESF theory. A specific focus will be on developing codes capable of handling the insertion or deletion of t zeros in each run (or bucket) of zeros. This problem is closely related to the zero-error capacity codes introduced by Shannon in 1956. 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.

CDFI

CCNY CDFI 18

CDFI

CCNY CDFI 19

NSF

Artificial intelligence (AI), computer vision, and machine learning have seen advances in research, driven in part by the creation and sharing of large datasets. These types of datasets do not exist for the virtual reality (VR) research communities. This planning grant forms a framework for the creation of large VR-related datasets. Experts in the VR community will create, capture, and share these needed VR datasets. This data will include head, hand, and eye tracking data. They will collect information on user behaviors and interactions while using VR systems. These datasets will advance VR software development, techniques, and applications. This will allow software advancements like observed in the VR hardware consumer markets. A primary goal of this planning grant is to design and create a large VR dataset infrastructure. This project will address an important gap in virtual reality research. A team of experts in VR will help capture multi-modal VR data (e.g., head tracking, eye gaze) and set up sharing of this data. They will also track interactions and behaviors with VR systems. VR experts will administer standardized questionnaires within most VR applications including consumer applications. It is expected that this will result in ecological validity of the datasets. The project plans to capture and publish a large dataset to help form the framework for use by the VR community. The project will provide an open-source toolkit for researchers to capture their own VR datasets. It is a goal that they will then contribute their datasets back to the VR research community. A key part of this project is to form an advisory board of computing experts to guide VR research directions. The project will organize a full-day workshop to engage the VR research community. This will help to identify community needs and priorities to advance research. The participants of this workshop will support the development of this important infrastructure. 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.

National Institutes of Health

CCRP Initiative: NIH Countermeasures Against Chemical Threats (CounterACT) Basic Research on Chemical Threats that Affect the Nervous System (R01 Clinical Trial Not Allowed)

NSF

Design/resource variables in wireless systems come in two special flavors: Short-term, dealing with dynamic and immediate decision-making to meet current demands or optimize instantaneous utility, and long-term, focusing on strategic planning over longer time horizons, optimizing a given wireless system in a certain statistical sense. As a matter of fact, short/long-term decision variables most frequently coexist in wireless system design, ultimately asking for joint management and distribution of resources as standard as bandwidth, power, phase and spectrum over different time scales (or stages), so as to optimize performance relative to various criteria, and on the basis of information with varying levels of detail. In this project, the PI puts forward a new methodological framework for the systematic treatment of joint short/long-term stochastic wireless system optimization. Such problems are formulated as natural instances of 2-stage stochastic programming with continuous uncertainty, and are considered under a fully data-driven and minimally model-free setting, allowing for (hopefully) deployable optimization schemes for training optimal wireless systems in-the-field. Concurrently, the proposed research also addresses several open technical challenges in algorithm development for solving general 2-stage stochastic programming models. Therefore, the proposed research is expected to be of more general interest and generate broader impact beyond the area of wireless systems as well. The proposed research is divided in the following two major thrusts: 1) 2-Stage wireless system design under imperfect oracles, addressing fundamental challenges, namely, establishing stochastic (sub)gradient representations of optimal values of (second-stage, short-term) problems with complex (i.e., non-trivial) constraints, dealing with inexactness caused by nonconvex optimization solvers, and planning against imperfect Channel State Information (CSI) estimation at the absence of ground truth information. In sharp contrast to existing work, the PI's goal here is to achieve fully data-driven, model-free system optimization, under no assumptions on (expensive to fit and maintain) cascaded CSI models and network structure. 2) Policy Function Approximations (PFAs) for model-free 2-stage system design, exploring functional reductions of 2-stage stochastic programs for eliminating instantaneous second-stage optimization, and enabling joint short/long-term training realizable by leveraging finite-dimensional policy parameterizations (i.e., PFAs) for short-term system optimization. The PI will investigate the design of PFAs with trainable constraint embeddings, as well as model-free synthesis of optimal policies, i.e., domain-inspired PFA discovery and training via zeroth-order stochastic optimization. Lastly, the PI will exploit such PFAs for learning distributionally robust short-term resource allocations with individual robustness levels (e.g., at users/terminals), targeting system effectiveness and deployability. 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

Large networked systems -- ranging from communication networks and power grids to social and transportation networks -- exhibit intricate interactions and dynamic dependencies among the agents that constitute them. In many domains, decision-makers need to understand how interventions such as policy changes, infrastructural modifications, or emergency responses will impact the system before implementing the interventions. Conducting real-world interventions in complex systems can be prohibitively risky with inadvertent consequences. In such circumstances, hypothetical evaluations allow decision-makers to simulate interventions and assess their potential impacts without exposing the system to real-world risks. This is particularly important when interventions have irreversible or costly consequences, as it enables planning and preparation for adverse outcomes. The overarching goal of this project is to design a theoretically principled framework for performing accurate hypothetical interventions on complex systems and predicting their outcomes, thereby providing a reliable basis for planning and risk assessment. This is especially crucial in environments where erroneous predictions or suboptimal decisions could lead to significant performance, robustness, or safety consequences. The project consists of several educational components aimed at students at different levels (high school, undergraduate, and graduate) as well as contributions to the educational missions of the relevant technical societies. This project introduces new theoretical foundations for causal reasoning in complex systems, aiming to move beyond the limitations of traditional data analysis. Standard observational datasets primarily reveal what “did” happen, offering little insight into what “might” have happened under alternative circumstances. In contrast, causal inference empowers us to explore these counterfactual possibilities, the “what if” scenarios that are essential for anticipating how systems respond to different interventions. By simulating hypothetical changes, this framework supports systematic comparisons between competing strategies and allows us to estimate their potential outcomes. For example, identifying the most structurally influential components within a network, such as key nodes or links, enables planners to design interventions that maximize resilience, especially in the face of uncertainty or disruption. While machine learning (ML) has proven useful for uncovering statistical patterns in large-scale systems, these patterns typically reflect correlations rather than cause-and-effect relationships. Correlation implies mutual variation but does not clarify directionality or underlying mechanisms — that is, whether A causes B, B causes A, or a third factor drives both. As a result, ML approaches fall short when it comes to predicting how deliberate changes to one variable will affect others, limiting their utility in policy design or engineering control. Causal inference offers a middle ground between the interpretability of physics-based models and the flexibility of data-driven ML. It retains the ability to model directional influence and system dynamics while leveraging empirical data as ML does. 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

Understanding the forest understory is essential for effective forest management, biodiversity conservation, wildfire prevention, and environmental monitoring. However, traditional satellite and aerial remote sensing technologies are fundamentally limited in their ability to observe vegetation beneath the forest canopy due to occlusion and signal attenuation. As a result, critical indicators of ecosystem health, such as aboveground biomass carbon pools, combustible understory fuel loads, and other measures of biodiversity in the understory, remain largely unmeasured at scale. This project addresses this critical gap by envisioning a low-cost, scalable sensing system that uses radar and wireless communication technologies to detect and characterize the forest understory, complementing existing orbital and suborbital remote sensing systems. The project advances radar sensing and physics-aware modeling by demonstrating an IoT-powered forest observatory in real-world scenarios, and creates new educational and outreach materials, including open-source software and student training modules, to broaden the impact and accessibility of the research. The project outlines a new approach that bridges radar sensing and backscatter communication for environmental sensing via a novel channel modeling approach through vegetation and generalizable physics-aware models that characterize the effect of biomass on radar signals. The key intuition is that vegetative dielectric and moisture content will alter the RF signatures in both frequency and time domains as they penetrate through the vegetation medium. These signatures can be learned using complex physics-aware models as long as the RF reflections that carry this signature can be reliably separated, modeled, and interpreted. The project team will realize this vision through four inter-connected tasks: (i) formalizing the radar backscatter through multi-layer forests, which results in a radar forest synthesizer (ii) proposing physics-aware radar backscatter models that offer spatial characterization and mapping of forest understory biomass (iii) introducing an RF-coded tag design that can pair up with off-the-shelf radar platforms and serve as ground references for accurate understory characterization; (iv) Fully evaluating the end to end system in wide-area testbeds and demonstrating the accuracy of characterizing forest understory. 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

Remote health monitoring is highly promising for big data-driven precision medicine, through conveniently and obtrusively tracking health conditions of people. However, when the sensing device is placed off-body for remote monitoring, the captured human signal is usually very weak. This is because the signal quickly decays when it propagates from the human body to the device. Further, the signal of the target person may be interfered if there are more than one person in the environment. Targeting these crucial challenges, this project will advance the science of high-fidelity remote health monitoring, through efforts on innovating the remote signal sensing and decoding system architecture. This project will greatly advance the national health towards pervasive, high-fidelity, and long-term big data establishment. More specifically, this project will design a novel deep senor array decoding system, which leverages the data-driven deep learning algorithm to decode the noisy and weak signal, without needing a reference signal used for propagation-induced distortion estimation. Besides, the multi-sensor spatial information will be leveraged by deep learning to boost the signal fidelity and recover the signal-of-interest from noise and interferences. The project will further contribute to research-education integration through new course development, new pedagogy practices, curriculum enhancement, and broad student training. The PI will continue broadening the participation of undergraduate students as well as K-12 students thereby effectively training the next-generation engineers and researchers. This project will innovate a novel deep sensor array decoding system, which can decode the signal-of-interest from the noisy and weak signal remotely captured, towards promising remote health monitoring and precision medicine big data. The multi-sensor signal captured by a sensor array, will be analyzed by the deep learning algorithm to learn the noise patterns, suppress the noise, and decode the high-fidelity signal. This data-driven approach does not need the reference signal that is usually used for propagation-induced distortion estimation, thereby enabling intelligent and convenient signal decoding. The spatial dynamics captured by the sensor array encode complex information about the signal-of-interest, can be effectively learned with the deep learning-empowered signal decoding. Besides, the deep learning algorithm will learn to separate the signal-of-interest if there are more than one person in the environment. The specific signal patterns for the target user will be learned and used by the deep learning algorithm to mine the target-relevant patterns in the multi-sensor signal captured. The proposed system architecture will be further evaluated with real-world experiments, to demonstrate the generalizable innovation and the effectiveness of the system. The novel system architecture will broadly contribute to various remote health monitoring applications, advance national health with pervasive and convenient big health data establishment, and promote the science on deep sensor array decoding for high-fidelity remote health monitoring. 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

Three-dimensional representations of scenes are of growing importance. For example, robots and autonomous vehicles rely on 3D mapping for path planning and safe navigation, and augmented reality systems use 3D scene models when they create visual overlays with useful information. These representations are also used in many other fields, such as surgical planning, architecture, and manufacturing. The prevalent technologies to create 3D representations emit laser light toward a scene and detect reflections from the scene. Among these technologies, frequency-modulated continuous-wave (FMCW) lidar is notable for achieving very good distance accuracy while also giving the instantaneous velocities of scene objects. The challenges with FMCW lidar, however, are mostly related to the complexity and cost of the hardware. One usually needs precise control of the laser’s frequency and very fast electronics at the detector. Broadly, the goal of this project is to introduce new concepts and algorithms that improve the performance of FMCW lidar or maintain current performance while loosening the requirements on the hardware. These developments may contribute to improved safety and lower cost for robots and autonomous vehicles. The basic conventional signal processing in FMCW lidar is undoubtedly clever. It reduces the estimation of distance and velocity to frequency estimation problems, and these can be solved by finding the position of the maximum magnitude of a discrete Fourier transform. In emphasizing simplicity, this processing neglects both robustness to phase noise of the laser source and the cost of having a high sampling rate at the receiver. While some methods have been developed to mitigate phase noise, the role of the sampling rate and the reduction to a pair of frequency estimation problems has been essentially unquestioned. This project examines FMCW lidar signal processing from first principles. Coherent detection produces a certain continuous-time complex interference signal, and its entire digitally sampled version is informative about the delay and Doppler shift—not only the segments that have constant frequencies. This project seeks methods to use the whole signal to estimate delay and Doppler shift as well as possible. Preliminary results demonstrate that aliasing in the digital sampling need not be disastrous; this leads to an increase in unambiguous range. Furthermore, setting the proper end goal of delay and Doppler estimation—as opposed to the arguably misguided intermediate goal of constant beat frequency estimation—provides improved robustness to noise. The specific goals include the introduction of estimation methods with good performance at moderate computational complexity and methods for use with nonlinear frequency modulation. 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

Advanced long-range imaging systems are critical for defense missions involving tracking moving targets and identifying adversarial objects, and astronomical imaging applications for ground-to-sky observations. Today’s long-range imaging systems are often equipped with adaptive optics to compensate for the distortions caused by the atmosphere. At the core of adaptive optics is the wavefront estimation problem where one needs to recover the phase information from the measurements. However, existing hardware solutions are limited by the resolution and hence they cannot retrieve high-order aberrations, whereas computational techniques generally require multiple measurements to ensure uniqueness. These, in turn, put many restrictions on the problem where it is generally difficult to estimate wavefronts involving moving targets. This research aims to advance wavefront estimation techniques with the goal to ultimately allow wavefront estimation from a single image. Achieving this technical goal will transform today’s long-range imaging systems, hence supporting new defense and space applications. The educational activities of the project stress on workforce development by training scientists and engineers for critical missions in national security, space exploration, and scientific imaging. The technical approach taken by this project is to develop an optics-algorithm co-design framework through neural mappings. By simultaneously seeking the optimal geometry of the aperture and recovering the phase using a new set of neural representations, the project positions itself as a potential solution for ultrafast wavefront estimation. The research consists of three thrusts: (1) theoretical foundations which study the optimality, symmetry breaking, and neural representations; (2) dynamic imaging conditions which involve the development of new models, recoverability analysis, and the decomposition of motion trajectory and phase aberrations; (3) computational and optical applications involving optical neural networks and phase-based image deconvolution algorithms. On the education front, the project supports new course development in camera physics, optics, and machine learning. A summer outreach program on machine learning and computational imaging is planned to serve students in grades 9-12. 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.

NIAID - National Institute of Allergy and Infectious Diseases

The contractor will perform Key experimental approaches which include isolation of MHC-bound peptides from targeted tissues by mass-spectrometry, paired TCRa/TCRb receptor single-cell sequencing, large-scale functional screening of TCR libraries, multiplexed omics and FACS studies of T cell function, and computational prediction of T cell cross-reactivity. Epitopes recognized by effector, memory and regulatory T cells will be characterized, including those derived from self-proteins, endogenous retroviruses, and posttranslationally modified antigens. Diseases investigated include a spectrum from full-fledged autoimmunity, to environmentally-induced pathogenic T cell autoreactivity, to cases where T cell activation has been observed in the affected organ but the role in pathogenesis remains to be resolved.

NCI - National Cancer Institute

Project Summary Adoptive cell therapy (ACT) treatment, in which cancer patients are re-infused with expanded autologous tumor-specific CD8+ T cells, has demonstrated some success in treating solid cancers like melanoma. However, the approach's overall complexity, cost, and insufficient clinical efficacy have limited its widespread application. To overcome these barriers, recent research has shifted towards innovative technologies, such as artificial antigen presenting cells (aAPCs), which offer a more adaptable and cost- effective solution for enhancing T cell responses for use in ACT. The goal of our proposed project is to advance this field by developing a cutting-edge aAPC platform, termed the artificial lymph node (aLN), that is specifically designed for the in vivo generation of CD4+ T cells. This system will consist of two distinct technologies: (1) MHC II-based aLNs to study and promote the generation of CD4+ cytotoxic T cells and (2) combined MHC I and II aLNs for the expansion of helper CD4+ T cells to improve CD8+ T cell function. The platform is made from a particulated hyaluronic hydrogel, that is conjugated with the three signals required for optimal T cell activation and expansion. We will investigate the effects of biomaterial properties and signal incorporation on in vitro T cell activation, as well as gain insight into in vivo antigen-specific T cell activation in a tumor-burdened host. We will develop advanced aLN microparticles (MPs) for in vivo injection to improve T cell activation for cancer immunotherapy. We will first explore biomolecular cues, focusing on the effects of antigen-specific stimulation with class II peptide-MHC molecules and examining how physical properties like ligand density and stiffness affect CD4+ T cell activation, proliferation, and cytotoxic function. We will also evaluate the effects of different signal 2 and signal 3 on CD4+ T cell phenotype and function, and refine aLN MP properties for effective human CD4+ T cell expansion using PADRE. Next, we will create aLN platforms that stimulate both CD4+ and CD8+ T cells to investigate and enhance CD8+ T cell function. We will analyze the impact of CD4+ T cell priming on CD8+ T cells and compare various aLN formulations to optimize co-culture conditions. This will guide further optimization of conditions for effective co-culture of human CD4+ and CD8+ T cells. Finally, we will test the optimized aLN MPs in vivo, evaluating their ability to generate and expand antigen-specific T cells and assess their efficacy in B16 murine melanoma models. If successful, this approach will produce a novel acellular platform for in vivo generation of effective CD4+ T cell responses, with the ability to enhance immunotherapy outcomes.

NCI - National Cancer Institute

Project Summary Few therapeutic options exist for triple-negative and heavily treated, hormone receptor-positive metastatic breast cancer (BC) patients. Antibody-drug conjugates (ADCs) improve overall survival for BC patients with low-HER2 expressing tumors by [Trastuzumab deruxtecan (T-DXd), Datopotamab deruxtecan (Dato-DXd), or Sacituzumab govitecan (SG)], but these strategies fail when HER2 targeting is not possible. Moreover, most patients who qualify and respond to T-DXd, Dato-DXd, or SG will relapse or progress, leaving even fewer therapeutic options available. Novel treatments that improve outcomes for patients who relapse after the current standard of care ADCs remain a critical clinical need. We propose a novel ADC-based therapeutic platform for aggressive and resistant BC to address two issues that continue to hamper the development and adoption of ADCs for clinical use: high treatment-associated toxicities due to on-target, off-tumor effects and poor tumor penetration. We will overcome these challenges with two specific technical advances: (1) development of conditionally-active antibodies with enhanced binding to antigens in the tumor microenvironment to minimize impacts on healthy tissues, and (2) drug conjugation via cyclic peptides, including an Arg-Gly-Asp tumor-penetrating peptide, and a neutrophil-elastase resistant linker (EGCit) to enhance drug penetration into tumors. To evaluate our ADC, we will target CD44 variant 9 (CD44v9), a marker associated with poor patient outcomes and overexpressed in the subgroup of BC patients who are resistant or unresponsive to HER2-targeted therapy; notably >50% of all BC are Her2-low and CD44v9+. We hypothesize that antibodies binding the novel CD44v9 target and engineered for optimized selectivity and payload penetration will effectively home to and kill aggressive breast cancer. We will achieve this objective by developing tumor-selective antibodies binding CD44v9 (Aim 1), creating anti-CD44v9 ADCs equipped with tumor-penetrating peptides (Aim 2), and evaluating the efficacy of the ADCs and CD44v9 antibodies in orthotopic and humanized mouse models of HER2-negative BC and BC that is resistance to FDA-approved ADCs (Aim 3). This project is technically innovative for its development of conditionally-active antibodies, the use of tumor- penetrating peptides, the introduction of a novel linker, the evaluation of CD44v9 as a novel target, and the combination of these elements into a single ADC. The results will provide proof of concept for a novel ADC platform for aggressive or drug-resistant BC and provide mechanistic insights into the biological role of CD44v9 in BC. When successful, the newly developed ADCs will offer a critical path forward for patients with aggressive BC who have limited treatment options.

FDA - Food and Drug Administration

PROJECT SUMMARY The long-term goal of this research is to change the prognosis of relapsed or refractory (r/r) T-cell Non-Hodgkin Lymphoma (T-NHL) using next-generation chimeric antigen receptor T-cell (CART) immunotherapy. CART therapy has led to remarkable clinical outcomes in B-cell malignancies, but the needle has not moved for most other cancers. Relapsed or refractory T-NHL are considered mostly incurable with available therapies and present a particular challenge for antigen-specific T-cell immunotherapy since normal and malignant T cells largely express the same surface antigens. Our group developed a novel approach to circumvent CART fratricide by deleting the pan T-cell antigen CD5 in T cells that are transduced to express an anti-CD5 CAR. Of note, our work demonstrated that the deletion of CD5 also enhances T-cell effector function by enhancing CAR signaling. The present project seeks to test this discovery in a first-in-human clinical trial to evaluate the feasibility, toxicity, and efficacy of CD5 deleted anti-CD5 chimeric antigen receptor CART for T-NHL. The central hypothesis of this grant is that CD5 deleted anti-CD5 CAR T cell immunotherapy is safe and can lead to potent and long-term clinical responses against T-cell lymphomas. Moreover, this research will explore whether the CD5 KO untransduced (CAR-negative) T cells from the CD5 KO CART5 product can provide short-term T-cell immunity in case of CART5 toxicity against the normal T cells that express CD5. The feasibility of this proposal is underpinned by our preliminary results and our Institution’s extensive expertise in translating CART therapies. A dedicated study team was formed with experts with complementary expertise, including the proposal’s principal investigator (Dr. Barta, clinical lead), co-PI (Dr. Ruella, correlative lead), biostatistician (Dr. Hwang), clinical research staff, and laboratory personnel to ensure the timely success of this proposal. Financial support has been secured from viTToria biotherapeutics, Inc, who serves trial sponsor. The central hypothesis will be tested by pursuing two specific aims. In Aim#1, the phase I clinical trial will be performed to evaluate feasibility, toxicity, and efficacy of autologous CD5-deleted anti-CD5 CAR T-cells for r/r non-leukemic T-NHL. The trial will utilize an Bayesian optimal interval design to establish the recommended phase II dose. In Aim#2, two working hypotheses will be tested using clinical samples obtained from Aim#1: i. CD5-deleted CART5 can potently expand and infiltrate tumors; and ii. CD5-deleted untransduced cells presented in the product can persist in the blood, limiting T-cell aplasia. Several laboratory techniques will be used for the correlative studies, including will flow cytometry, single-cell transcriptomics, cytokine profile and digital spatial profiling to study the T-cell dynamics and function in the blood and tumors. The proposed research is highly relevant to public health because patients with r/r T- NHL have no available cures with standard non-cellular therapies. This study will significantly impact the field of cancer immunotherapies by developing a novel potent anti-T-cell lymphoma immunotherapy and such address an unmet need in a rare disease.

Department of Fleet and Facility Management

COMMUNITY DEVELOPMENT BLOCK GRANT CORONAVIRUS (CDBG-CV)

Department of Public Health

COMMUNITY DEVELOPMENT BLOCK GRANT CORONAVIRUS (CDBG-CV)

Department of Family and Support Services

COMMUNITY DEVELOPMENT BLOCK GRANT CORONAVIRUS (CDBG-CV)

Department of Planning and Development

COMMUNITY DEVELOPMENT BLOCK GRANT CORONAVIRUS (CDBG-CV)