Grants

Chinatown International District - Through the Eyes of a Tiger

Our team of photographers have come together to gather a body of work which documents what's going on in the Seattle Chinatown/International District (CID) during 2022, the lunar Year of the Tiger. The goal is to explore the space and capture moments of what's occurring within the boundaries of this historic neighborhood. The project will include a website displaying donated photos, a gallery exhibition that is free and open to the public at the Nisei Veterans Committee Hall, and a free workshop on How to shoot photos in the Chinatown-International District. The anticipated project close out date is July 31, 2023.

Seattle Asian American Film Festival

The Seattle Asian American Film Festival will screen free films at Hing Hay Park, 423 Maynard Avenue South on two successive Saturdays, August 18th and 25th, 2018. The films are targeted to Asian Americans in the surrounding community with the film on August 18th being screened in it's original Chinese language. The screening on August 25th will focus on the Japanese American internment during WWII. The films will be preceded by live performances and arts and crafts.

Cider Pressing Planning Committee

The Cider Pressing Planning Committee will host a free community event to celebrate the bounty of autumn. It will feature cider pressing using apples from neighborhood orchards, food, and live entertainment. It will be held on September 22 from 10 am - 3 pm on 14th Ave NW between NW 95th St and NW 100th St.

NSF

Humans and machines make sense of their surroundings first and foremost using vision, and vision is limited when there is a lack of light in the familiar visible spectrum, with wavelengths from 380 nm (violet) to 750 nm (red). However, light is often plentiful in situations when visible light is lacking (such as at night), but most of it is at wavelengths far beyond the red end of the visible spectrum. This long-wave infrared (LWIR) light is responsible for the images produced by common thermal cameras, which are used in search-and-rescue operations, law enforcement, building inspections, and wildlife observation. Those uses are based on sensing only temperature or heat. Broadly, the goal of this project is to do more with LWIR sensing, such as three-dimensional imaging and remote sensing of air composition and temperature. These new capabilities may contribute to improved security, navigation, pollution monitoring, and aeronautical safety. Additional broader impact aspects of the work include workforce training at the high-school, undergraduate, and graduate levels. This project is focused on formulating and solving new inverse problems based on well-studied aspects of radiative transport. The central challenges come from these inverse problems being underdetermined because the degrees of freedom in emissivity, transmittance, and reflected light increase with the number of spectral channels measured. The project will combine physics-based approaches such as parametric modeling of transmittance with learning-based exploitation of scene structure. The specific goals include improving passive ranging and demonstrating the measurement of air temperature and gas concentrations at a distance. 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

Artificial Intelligence (AI) systems are rapidly advancing in capability, driven by growth in complexity, model size, and training datasets. However, methods to understand the behavior of these models have not scaled commensurately. Furthermore, there remains limited understanding about how important individual data points are to the model behavior. In order to ensure that these models can be trusted and safely deployed in sensitive applications like autonomous navigation, national defense, and medicine, it is critical to develop a more profound understanding of how they work. Greater transparency into the decision-making process of AI models can be used to accelerate scientific discovery in fields like protein design and drug discovery by revealing novel patterns in complex data. Clearer explanations also enable direct model improvement with the goal of building more robust and trustworthy AI systems. This project will develop a powerful method for making AI predictions understandable and explainable using token masking techniques that combine coding theory and signal processing. This project aims to advance the understanding of Artificial Intelligence (AI) models by leveraging the discovery that AI models exhibit sparsity under spectral representations such as the Fourier transform. By employing ideas from channel coding theory for masking, fast spectral methods, and tools from signal processing and error correction coding, the project will develop scalable algorithms to identify significant input interactions and features providing faithful explanations of AI model behavior. The project will also aim to understand the influence of different training data samples on model predictions, as well as the impact of data imbalances on the dynamics of AI applications in order to create well-designed data acquisition mechanisms. In critical applications such as healthcare, these methods will allow one to validate an AI-generated diagnosis by efficiently identifying the most salient features in a patient's medical record. Furthermore, this framework will help identify when a model relies on irrelevant or harmful data features, providing a path to align the model toward more robust and trustworthy outcomes. 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

Graphs encode relationships between items. Data in the form of graphs is widespread across the sciences, technology, and everyday life. Examples include social networks indicating friendships between individuals, protein interaction networks capturing compatibility of proteins, and flight networks containing links between airports. Graph data has the potential to play an integral role in machine learning systems, yet it remains unclear how to best make use of this data. The goal of this project is to develop a mathematical foundation to guide the principled use of graph data in a wide variety of machine learning tasks. Due to the basic nature of the research, potential downstream applications of new algorithmic insights span a multitude of applied domains; an example is improved therapeutic drug discovery through accurate predictions of drug efficacy with significantly fewer costly and time-consuming experiments. The project involves the mentoring of both PhD students and undergraduate researchers. The results generated from this project will be integrated into advanced undergraduate and graduate courses on probability and algorithmic statistical inference. Many real-world networks are accurately represented by graphs with underlying geometry: for instance, in a social network, the nodes are associated to a list of features (e.g., the individual's location, age, hobbies, personality, etc.), and edges between pairs of nodes are formed as a function of their unobserved feature vectors. The project develops principled methodology for optimally exploiting structure in the latent space underlying a graph in order to carry out machine learning tasks. The first of three project components characterizes how the geometry of the underlying space affects structural or combinatorial properties of the graph. The second component studies the problem of recovering latent feature vectors from an observed graph, and aims to derive optimal algorithms for a wide variety of latent space graphs. The third component investigates semi-supervised learning with graph data, and aims to find computationally efficient algorithms achieving the best possible prediction accuracy. 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

This project explores how information behaves in large-scale data storage systems that are designed to recover from hardware failures. Modern storage devices, such as glass-ceramic platters, arrange data in flat, grid-like patterns. To protect against data loss, information is encoded in a way that allows missing pieces to be reconstructed from surrounding bits, using a pre-set recovery rule. Over time, these systems handle vast and varied patterns of data. The research aims to understand how such data patterns evolve and organize themselves, from a statistical perspective. By identifying typical patterns and behaviors, the project will help improve the efficiency, reliability, and adaptability of storage systems—ultimately supporting better data preservation and easier access. In technical terms, the storage medium is modeled as an assignment of bits to nodes of an infinite two-dimensional grid, where the content of each node is determined by its neighbors according to a fixed recovery rule. A collection of such assignments—called a recoverable system—consists of all configurations satisfying this rule and forms the central object of study. The project aims to identify which recovery rules support systems that store the largest volume of the data on the medium, formalized as the topological entropy of the system. Introducing a temperature parameter into the model further increases in data density, assuming the system can tolerate a specified probability of errors. The system is defined through interaction energies between neighboring sites, with recoverable configurations corresponding to ground states. A key goal is to analyze equilibrium (Gibbs) distributions on the configuration space and to identify temperature regimes where phase transitions occur, leading to qualitatively different patterns of data stability. As another goal, the project aims at quantifying the system’s ability to retain information as it repeatedly restores data using the recovery rule over time, modeled as a Markov process on the space of the configurations. 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

Distributed intelligence with large amounts of local measurement data hinges on the design of devices that are capable of sensing, processing, and exchanging local information. Reliable decision-making in a collective manner is, however, a challenging task in resource-constrained scenarios, where devices are limited by communication costs and/or processing power. This project aims to develop a new framework for designing efficient decentralized algorithms such that the average proportion of wrong decisions in the network is bounded by a prescribed target threshold. This line of research has implications for a broad range of real-world applications, including environmental monitoring using battery-powered mobile sensors, coordination of unmanned aerial vehicles for target tracking, and multimedia wireless sensor networks in surveillance. The project will also provide mentoring and training of future algorithm designers. This project investigates the structural properties of optimal decision rules under the false discovery rate (FDR) control, which provides guidance for new co-design of summary statistics and aggregation mechanisms, thereby enabling efficient decentralized processing in resource-constrained environments. The research program will explore three main thrusts: (i) develop communication-efficient algorithms (measured in bits) for multi-hop networks with provable FDR control; (ii) characterize computation-efficient approximations of the optimal decision rule in both the finite-sample and asymptotic regimes; and (iii) develop distributed feature selection with FDR control when all the features are shared among devices, focusing on privacy, robustness, and computation efficiency. 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

Emerging extended reality technologies, including augmented reality, mixed reality, and virtual reality, demand high-speed low-latency communication to support interactive immersive applications such as three-dimensional (3D) telemedicine and video conferencing. However, achieving both high speed and low latency can lead to a high bit error rate, which in turn reduces communication reliability. Thus, it is essential to develop a new error detection and correction coding solution that can tolerate communication errors in such scenarios. In communication systems, bit errors exhibit varying levels of semantic significance. Errors with low significance can be tolerated, while those with high significance must be corrected. This project will explore topological information from source data for error detection and correction in communication systems, which can ensure data fidelity at the topology level rather than the bit level, aiming to detect the significance of errors in the received data and correct them by recovering the original topological information. This approach offers new insights into communication errors and can potentially enable the development of novel codes for next-generation communication systems. In addition, this project will provide research opportunities for both graduate and undergraduate students and will leverage outreach activities at the University of Nebraska-Lincoln to share research outcomes with K-12 students and teachers using specially developed educational modules. In this project, topological data analysis, particularly persistent homology and persistence diagrams, will be used to extract and encode topological information from source data. The topological information captures high-level data relations, which can be more compact and semantically meaningful than bit-level relations. This project will study the feasibility and evaluate the performance of topological error detection and correction using point cloud data as a primary data modality. A foundational relation between bit errors and topological errors will be established, aiming to guide the development of topological error detection algorithms using uncompressed persistence diagrams to detect and evaluate the significance of errors. In addition, topological error correction algorithms will be designed by optimizing persistent homology functions to correct significant errors in the received data. 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

Machine learning systems have made revolutionary advances in several areas, including (but not limited to) automated speech and image recognition, scientific discovery, human health and national security. These advances have been made possible in large part by the training of high-capacity models that are able to capture and infer complex relationships between exhorbitant amounts of data, such as images, video, and speech. Such training is quite resource-intensive and failure-prone and typically requires the deployment of large groups of computers that operate collaboratively to achieve the overall objectives. For instance, by conservative estimates, the training of current state-of-the-art models for language understanding consume enough energy to power over one thousand average US households for a year. Moreover, a rule-of-thumb within distributed computing states: "failures are the norm, rather than the exception". This project will investigate resource-efficient and fault-tolerant schemes for distributed model training within machine learning. Specifically, the training time depends on the reliability and speed of the computers and the speed of communication between them. This project will examine techniques for simultaneously increasing both the reliability and speed of the process. If successful, this will result in significant energy and monetary savings across the board in scenarios where machine learning is routinely deployed. The ability to work with large-scale computing clusters is an essential skill for the workforce, and this project will help train undergraduate and graduate students in such techniques. The team of researchers will volunteer for mathematics tutoring activities as part of the CyMath initiative at Iowa State; CyMath offers free and open-to-all, after-school math tutoring for elementary and middle school students in Ames area schools. Overall, the goals of this project will lead to the acceleration of corresponding machine learning driven advances in a variety of fields, e.g., science and human health, and contribute towards the US economy and society. Distributed machine learning model training typically involves minimizing a loss function that depends on the training dataset with respect to a parameter vector. The number of parameters in many problems of real-life interest can range from hundreds of millions to billions. Such training is the driver of key technologies such as deep neural networks and large language models. In these systems, the workers are required to compute gradients on the data points assigned to them. Depending on the underlying architecture, the workers either communicate to-and-from a central server or exchange messages amongst themselves to perform gradient descent over several iterations. In addition to worker-node computation, communication is well recognized as being a significant part of the overall training time. It is well recognized that worker nodes (especially within cloud platforms) are prone to straggling (slow-downs and/or failures). The foundational goal of this project is to investigate failure/slowdown-resilient and communication-efficient schemes for distributed model-training for different classes of learning architectures. This will be achieved by introducing redundancy in the assignment of data points to the worker nodes and using coding-theoretic ideas for the recovery of the gradient, either exactly or approximately. The research team will examine the design of numerically stable and communication-efficient schemes that leverage the work performed by slow (as against failed) workers. They will also research classes of schemes that vary depending on the amount of system knowledge across the workers and the assumed communication between them. A key goal will be to provide rigorous guarantees of the quality of the gradient that is recovered by the system. The successful completion of the goals of this project will result in significant reductions in training times of distributed models and corresponding resource savings. 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

Many complex and interconnected phenomena in the world - such as social media, sensor grids, and brain connectivity - can be modeled using graphs or networks. Unlike classical signal processing, which works with data regularly arranged in a homogeneous space (like audio or images), graph signal processing (GSP) analyzes signals that lie on irregular graphs or networks. Important outcomes of such analysis include detecting patterns, detecting and reducing noise, and visualizing the network. GSP has become a vibrant field of research in engineering and mathematics due to its applicability to a wide range of real-world problems, such as data analysis on sensor networks, biological networks, and neural networks. In this project, the investigator uses a blend of mathematical theories and techniques to develop the theoretical underpinning and possible new applications of GSP, especially for the case of large dynamic networks that evolve over time. The investigator plans to couple this research with graduate student mentoring, organizing scientific workshops, and outreach in the scientific community. In this project, the investigator aims to leverage a blend of techniques from harmonic analysis, functional analysis, and graph-limit theory to address challenges in information processing, particularly in the theoretical underpinning of GSP. The goal is to develop a theory that is applicable to a wide range of large dynamic networks. The relatively recent theory of graph limits and graphons provides a valuable non-parametric approach to modeling networks, particularly for stochastic networks. Indeed, graphons represent random processes that generate networks, and networks produced by the same graphon have similar large-scale features. Many large networks that arise naturally are manifestations of an underlying (hidden) spatial reality and can be efficiently modeled using Cayley graphons. In this project, the investigator develops signal processing on Cayley graphons as well as instance-independent GSP for samples of Cayley graphons. Current instance-independent graphon-based signal-processing methods apply to only dense undirected graphs. To develop a theory that applies to a large class of (possibly sparse or directed) networks, the investigator plans to address several theoretical challenges, such as convergence of spectra for sparse/directed graphs and spectral theory of nearly Cayley graphs. 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.

Canadian Institutes of Health Research (CIHR)

Foundation Grants provide long-term funding to established health research leaders for programs of research. Grants range from $500,000 to $1.4 million per year for five to seven years.

Canadian Institutes of Health Research | Instituts de recherche en santé du Canada

The datasets found below contain information regarding investments in health research made by the Canadian Institutes of Health Research (CIHR) for projects that received funding within specified time periods (fiscal year or quarters within) since the 2000-2001 fiscal year. Fiscal years at CIHR run from April 1st to March 31st. Please note that much of the information in the files below is provided in the language in which it was submitted by the researcher. Significant changes to the way information is reported on this page were made in September 2024 and are applicable to all files published henceforth. Changes were made to improve the user experience and increase data accessibility. A more robust and informative metadata file was introduced as well as additional data fields. There is now one amalgamated file provided per fiscal year, organized into the following five tabs: Grants & Awards - This dataset contains general information about the funded projects. There i

Canadian Institutes of Health Research (CIHR)

Project Grants support researchers at any career stage to build and conduct health-related research and knowledge translation projects. Grants range from $100,000 to $750,000 per year for one to five years.

Canadian Institutes of Health Research (CIHR)

Team Grants bring together multidisciplinary teams to address pressing health research questions through collaborative approaches. Funding varies by competition but typically supports large-scale, multi-year research programs.

NINDS - National Institute of Neurological Disorders and Stroke

PROJECT SUMMARY Malformations of cortical development (MCDs) are neurological disorders characterized by altered organization and function of cortical neurons, often leading to intractable epileptic seizures. Somatic mutations in more than 60 genes have been found in MCDs, with pathogenic variants causing focal impairment of cortical function. This genetic heterogeneity is reflected in the delineation of multiple types of MCDs, including focal cortical dysplasia type I (FCDI) and type II (FCDII) and Tuberous Sclerosis Complex (TSC). These MCD classes have overlapping and distinct genetic bases and cortical neuron phenotypes. For example, FCDII and TSC exhibit hyperactive mTORC1 signaling due to activating mutations in pathway components (or inactivating mutations in negative regulators such as TSC1 and TSC2), while FCDI does not exhibit mTORC1 alteration. Critically, the mechanisms by which MCD-associated mutations drive disease pathogenesis remain incompletely understood, and the degree to which these mechanisms are shared across MCD types is unclear. Better understanding the basis of MCDs promises to not only shed light on the fundamental biology of cortical development, but also to offer improved diagnostic tools and therapeutic strategies for MCD patients. Here, we propose a new model for MCD pathogenesis based on our recent finding that dysregulation of primary cilia is a shared feature of gene variants responsible for FCDI, FCDII, and TSC. This model is based upon the convergence of two unbiased genetic studies: a consortium-led sequencing study that identified 69 genes mutated in MCD patients and a genome-wide CRISPR activation screen we conducted that identified a novel pathway for cilia disassembly. Based on the significant overlap of these gene sets and the established importance of cilia in cortical development, we hypothesize that aberrant disassembly of primary cilia is a shared pathological basis for MCDs. In support of this hypothesis, our preliminary data show that a FCDI- associated variant in SARM1 induces cilia loss, while Sarm1 inhibition restores cilia in cells harboring mutations in SARM1 or in FCDII gene TSC2. From these findings, several questions emerge, including: how does FCDI-associated mutation of SARM1 impact cilia and cortical development in vivo; what is the functional relationship between cilia loss induced by Sarm1 versus by mTORC1; can inhibition of cilia disassembly mitigate FCD-associated pathology; and do other ciliary regulators contribute to MCDs? We will address these questions in three aims: 1) test the effect of an FCDI-associated SARM1 variant on neuronal ciliation, cortical development, and seizure activity, 2) test the hypothesis that mutation of FCDII gene TSC2 alters neuronal development and function through Sarm1-mediated cilia disassembly, and 3) use CRISPR screening to define additional genes that promote cilia loss in MCD. These studies will be performed through a collaborative effort of the Breslow and Bordey labs that combines their unique and complementary expertise in cilia, ciliopathies, functional genomic screening, MCD pathogenesis, and murine cortical development and function.

NIA - National Institute on Aging

SUMMARY Down syndrome (DS), also known as Trisomy 21 (T21), is a common genetic disorder caused by an extra copy of chromosome 21. This additional genetic material disrupts multiple molecular pathways and leads to a range of health problems, including cognitive impairment. The circulation of cerebrospinal fluid (CSF) through the brain's ventricular system is crucial for maintaining brain homeostasis. This involves distributing nutrients, eliminating waste, managing fluid pressure, and regulating ventricular neurogenesis. The flow of CSF is supported by the coordinated beating of multiple cilia of the ciliated cells of the ependymal lining of the ventricles. Recent studies using mouse models with trisomy of genes orthologous to those on human chromosome 21 have revealed various defects characteristic of ciliopathies, suggesting a potential link between DS and impaired ciliary function. In the brain, this link may manifest as alterations in ependymal architecture, morphology and arrangement of ciliated cells, ciliary motion, fluid flow, and adult neurogenesis. Our collaborative team has developed novel techniques for live imaging and analysis of ciliated cell arrangement, ciliary motion, ciliary beat frequency (CBF), and CSF flow in brain ventricles. These include innovative algorithms for analyzing ependymal architecture in the lateral ventricles, arrangement and planar polarity of multiciliated ependymal cells, automatically quantifying CBF, and analyzing cilia motion, flow velocity, and flow directionality in live imaging experiments. These methods have enabled us to uncover previously unrecognized features in the arrangement and function of ciliated cells in the ventricular walls and to characterize ciliopathies in mutant mouse lines. Specifically, we discovered that in addition to the primary posterior-to-anterior flow along the ventricular wall, there are multiple local microflows with diverse trajectories exist there, including both direct and curved paths, which deviate from the main flow direction. We also found that across extensive areas of the ventricular walls, multiciliated cells align only locally with immediate neighbors, forming defined cell domains with diverging polarity; this arrangement likely facilitates the complex mosaic of the microflows, enhancing CSF distribution across the ependyma. We now propose to apply these advanced novel methods to analyze ependyma architecture, ciliary motion, and flow in DS mouse models. In our first specific aim we will determine the changes in the overall architecture of the ependyma (ciliated cell domain arrangement, translational polarity, neurogenesis) in an overlapping set of DS model lines, as compared to the mice of the background strain. In our second specific aim we will use live imaging to compare flow velocity and composition, ciliary motion, and CBF between DS models and control mice. Together, these experiments will help determine whether ciliary dysfunction may contribute to DS neurological phenotypes.

Cincinnati Ballet Company, Inc.

Multiple

Cincinnati Scholarship Foundation

Cincinnati Scholarship Foundation is an active grantmaking foundation based in Cincinnati, OH. Focus: general. Contact the foundation directly for current funding opportunities.

NYC Department of Cultural Affairs

Cinema Tropical, Inc.

Yetti Frenkel

Cinquain Poetry and Fold-Out Book Workshop

EDF

Cintas Capital

CINTAS CORPORATION

Cleaning Equipment and Supplies

CIORBA GROUP INC

Engineering and Research and Technology Based Services