Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1406&sort=-awardee_organization
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Uncertainty in transmission rates and the outcomes of social distancing, \"shelter-at-home\" executive orders, and other interventions have created unprecedented challenges to the United States health care system. This project will address these issues directly using advanced mathematical modeling from dynamical systems, stochastic processes, and networks. The mathematical models, which are formulated with the specific features of COVID-19 in mind, will provide insights that are critical to people on the front lines who need to make recommendations for intervention strategies and human-behavior patterns to best mitigate the spread of this disease in a timely manner. The project will train a postdoctoral scholar, a PhD student, and two undergraduate students in the research needed to solve these complex problems. The standard approach for epidemic modeling, at the community scale and larger, is compartmental models in which individuals are in one of a small number of states (for example, susceptible, infected, recovered, exposed, latent), with individuals moving between states. The COVID-19 epidemic can be modeled in this way, with resistance as part of the dynamics. The simplest examples of such models for large populations are coupled ordinary differential equations that describe the fraction of a population in each of the states. To model the stochasticity of infection and latency, models with self-exciting point processes can be fit to real-world data. This project compares the dynamical systems and stochastic models of relevance to COVID-19 transmission. The models also incorporate network structure for the transmission pathways. The project extends prior research on contagions on multilayer networks by incorporating multiple transmission methods and coupling between the spread of the contagion itself and human behavior patterns. The project leverages high-resolution societal mixing patterns in epidemics, as they influence both (1) observations and demographics of who has been diagnosed with COVID-19 and (2) who transits the disease, sometimes without being diagnosed.This award is co-funded with the Applied Mathematics program and the Computational Mathematics program (Division of Mathematical Sciences), and the Office of Multidisciplinary Activities (OMA) 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1934", "attributes": { "award_id": "2031187", "title": "RAPID: Dynamic Graph Neural Networks for Modeling and Monitoring COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "096Z", "7364", "7914", "9251" ], "program_officials": [ { "id": 5137, "first_name": "Wei", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2023-05-31", "award_amount": 114000, "principal_investigator": { "id": 5138, "first_name": "Wei", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The novel coronavirus, COVID-19, has become one of the biggest pandemics in human history and has generated lasting impacts on public health, society, and economy. The number of cases in the United States has passed 1 million with a total number of deaths over 50 thousand. There is an urgent need for research and development that can bring a predictive understanding of the spread of the virus, thereby enabling mitigation methods to alleviate the negative effects of COVID-19. Traditional epidemiological models usually take into consideration only a small number of features in building a prediction model, which may not be able to capture potential risk factors and effects of various intervention mechanisms of this new pandemic. In this project the investigators develop novel machine learning methods that can simultaneously model and predict the COVID-19 spread, detect and monitor risk factors, and evaluate effectiveness of interventions over time and space. The new model ingests and integrates heterogeneous and rapidly accumulating data across diverse sources, such as publications, news, census, social media, and outbreak observation trackers. It employs a new contextualized language model to accurately recognize named entities and relations from vast text data and build knowledge graphs to extract potential risk factors. A dynamic graph is constructed. Each location node may have a set of static and time-dependent attributes. Events, individual behaviors, social activities, interventions are mapped to activity nodes with edges connecting to the corresponding location nodes at the time. A novel dynamic graph neural network is trained to perform joint predictions of all locations over time. Activity nodes of significant attention weights represent major risk factors or effective intervention mechanisms. The project will result in public dissemination of the prediction model and all source codes, immediately benefiting the combat against COVID-19.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1964", "attributes": { "award_id": "2034809", "title": "RAPID: Social isolation during COVID-19: Effects on fear learning and implications for trauma", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [ "096Z", "1332", "7914" ], "program_officials": [ { "id": 5231, "first_name": "Steven", "last_name": "Breckler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2021-05-31", "award_amount": 116722, "principal_investigator": { "id": 5232, "first_name": "Naomi I", "last_name": "Eisenberger", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "In response to COVID-19, people across the United States have experienced extreme and abrupt shifts in their level of daily contact with others, forcing many people into a state of social isolation. This project examines whether the experience of social isolation amplifies the development of harmful and persistent fears in humans. The research further tests if social isolation reduces the ability to eliminate those fears once learned. This study utilizes a unique situational event (social isolation due to COVID-19), which cannot be replicated in the laboratory, to provide rare insight into how social isolation heightens the development of fear and if experiences of social connection can reverse these effects. This research will contribute to the growing understanding of the effects of social isolation on increasing fears and influencing the developmental course of trauma; it may also provide simple strategies to mitigate those effects.Research suggests that social isolation may influence long-term trauma by directly influencing fear learning processes. Socially isolated animals exhibit increases in fear responses that persist even when a threat is no longer present, are more likely to develop PTSD-like symptoms following trauma. Similarly, research suggests that socially isolated humans are at a greater risk of developing PTSD. To date, however, no work has directly examined the impact of extreme social isolation on fear learning in humans. Preliminary data suggests that feeling socially isolated appears to lead to persistent fears and poorer fear extinction, although these effects may be eliminated when individuals are reminded of their social support during fear extinction. This project will evaluate these hypotheses by sampling people from across the U.S. living in places where some form of ‘Stay at Home’ order is in place. The research examines whether the extreme situational social isolation brought about by COVID-19 leads to increased development of fear associations for neutral images paired with negative affective stimuli. The study also examines whether a two-week social connection intervention, during which individuals will be asked to do positive things for others to promote feelings of connectedness, improves fear extinction and decreases the development of fear associations in those most severely isolated. The findings of this work have the potential to shed light on how social isolation influences the course of trauma and reveal simple, low-cost, and accessible interventions to reduce this trauma that can be used immediately and in the future to ease the harmful side effects of necessary measures to address COVID-19 and similar events.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2013", "attributes": { "award_id": "2032310", "title": "RAPID: Biomimicry of SARS-CoV-2 and its consequences for infectivity and inflammation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7465", "7914" ], "program_officials": [ { "id": 5384, "first_name": "Steve", "last_name": "Clouse", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2021-06-30", "award_amount": 200000, "principal_investigator": { "id": 5385, "first_name": "Gerard", "last_name": "Wong", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This research addresses two critically important questions concerning the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the novel coronavirus disease (COVID-19): Why is this virus so infectious, and why does it sometimes cause lethal inflammation? It is now well-known that COVID-19 infections can spread from hosts early, before onset of symptoms, which implies efficient viral entry and egress. The root causes of COVID-19 induced inflammation are less known, but cognate effects have been seen in other superviruses such as the coronavirus for the original severe acute respiratory syndrome (SARS), and the 1918 pandemic influenza virus. This research investigates the basic molecular mechanisms of how parts of this virus can mimic the functions of defensive molecules from the human innate immune system which restructure membranes and control inflammation, thereby allowing the virus to achieve both of the above effects. The multidisciplinary nature of the research will also provide training opportunities for students at multiple levels.The overall goal of this project is to perform basic research that may help mitigate the current COVID-19 crisis by using artificial intelligence informed fundamental biophysics, immunology, and virology. The project objectives are two-fold. 1: use machine learning methods to identify molecules encoded in the genome of the SARS-CoV-2 virus that remodel the membrane for viral entry and viral shedding, and design ways to turn off this activity by mimicking molecules found in bats, which harbor many coronaviruses and therefore have more evolved defenses against them. 2: Identify molecules from the SARS-CoV-2 virus that mimic molecules from the human innate immune system which are known to mediate potent inflammatory responses, thus allowing potential downstream design of molecular approaches to alleviate this type of inflammation. These ideas will be tested using small angle x-ray scattering (SAXS) experiments performed at state-of-the-art 3rd generation synchrotron radiation facilities in the US, as well as traditional immunological and antiviral experiments, including those done at high containment biosafety level 3 facilities designed to handle SARS-CoV-2. Expected results from this basic research project can provide translational guidance for design of COVID-19 treatment strategies, even if current vaccine candidates fail.This RAPID award is made by the Cellular Dynamics and Function Program in the Division of Molecular and Cellular Biosciences, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2108", "attributes": { "award_id": "2032533", "title": "RAPID: Collaborative Research: Low-Cost, Non-invasive, Fast Sample Collection System for COVID-19 Viral Load Level Diagnosis: Point-of-Care and Environmental Testing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5677, "first_name": "Ron", "last_name": "Joslin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2021-12-31", "award_amount": 90000, "principal_investigator": { "id": 5680, "first_name": "Hossein P", "last_name": "Kavehpour", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5678, "first_name": "Robert N", "last_name": "Candler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5679, "first_name": "Nasim", "last_name": "Annabi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Pandemics can have a devastating impact on societies: collapsing local economies, halting trade, weakening national security and overwhelming healthcare capacity. To combat infectious pandemics, rapid, effective diagnostic testing combined with contact tracing and quarantine is needed. This will help officials manage infections while minimizing the effect of the disease on the economy, on society, and on our healthcare system. Further, effective sentinel monitoring of local environments can detect the presence of dangerous levels of virus, preventing mass spreading events. Unfortunately, the COVID-19 pandemic has exposed a critical weakness in health care security infrastructure: the deficiency in our ability to conduct rapid, simple, point-of-care diagnostic and environmental sample collection and testing. The goal in this research is to develop inexpensive, massively deployable rapid diagnostic and sentinel systems for detecting respiratory illness and airborne viral threats. Because the virus is transmitted through droplets in the breath, this system is expected to collect enough sample from one minute of breathing to be used in existing testing units. This technology is based on continuous dropwise condensation (CDC) which is capable of efficiently extracting particulate (viral) loads from humidified air within a minute. The collection system and supporting instrumentation is simple and can be readily integrated with a well-designed patient interface that is non-invasive, compatible with current rt-PCR sampling and which can be mass produced cheaply. Additionally, CDC, due to its surface collection method, can be modified readily into either a point-of-care, rapid diagnostic test, or into an environmental sentinel sampling/testing system.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2283", "attributes": { "award_id": "2113879", "title": "Collaborative Research: PIPP Workshop: Pandemic Readiness for Emerging Pathogens(PREP) to be Held February 15-19, 2021.", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [ { "id": 6261, "first_name": "Svetlana", "last_name": "Tatic-Lucic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-01", "end_date": "2023-01-31", "award_amount": 31442, "principal_investigator": { "id": 6262, "first_name": "Brian", "last_name": "Wood", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award will fund the Pandemic Readiness for Emerging Pathogens (PREP) workshop, planned for February 15-19, 2021. The PREP workshop will foster scientific discussion and catalyze innovation and partnerships to enhance our understanding of the challenges and potential solutions to rapid detection and assessment of emerging pathogens and infectious disease dynamics from the molecular to the ecological scale. Four panel topics are planned for this workshop: (1) The Challenges in Rapid and Accurate Detection and Assessment of Emerging Pathogens; (2) Approaches to Rapid and Accurate Detection and Assessment of Emerging Pathogens; (3) Monitoring Animal and Human Movements, High-Risk Interfaces for Disease Transmission, and At-risk Communities; (4) Data-Intensive Machine Learning and Modelling for Pandemic Preparedness. By integrating expertise in new sensor engineering, epidemiology, wildlife, and human health, ecology, quantitative social sciences, evolutionary biology, engineering, and computer science, new approaches in modeling, computational mathematics, data-enabled predictive science and decision making, and real-time intelligent sensing will be identified to transform infectious disease outbreak prediction capabilities. Subsequent to the workshop, the organizers plan to prepare reports summarizing the workshop for publication in high-profile general science outlets which can be used to inform the development of predictive intelligence for pandemic prevention.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2552", "attributes": { "award_id": "2027277", "title": "ATD: Algorithms for Threat Detection in Knowledge Graphs", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "ATD-Algorithms for Threat Dete" ], "program_reference_codes": [], "program_officials": [ { "id": 7336, "first_name": "Huixia", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-08-31", "award_amount": 607050, "principal_investigator": { "id": 7338, "first_name": "Andrea", "last_name": "Bertozzi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 7337, "first_name": "P. Jeffrey", "last_name": "Brantingham", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project develops new mathematical algorithms and models involving knowledge graphs. A knowledge graph represents what is known about a subject in the form of labeled nodes and edges. More than simply labeled data, knowledge graphs organize data according to high-level meanings and assign globally unique identification to each node in the graph to match real-world entities. Much work on knowledge graphs treats databases and queries. In contrast, in the context of threat detection, this project focuses on algorithms that identify latent information in the graph and predictive models associated with data on the graph. The project will involve a combination of mathematical methods for subgraph isomorphism detection, time series analysis, agent-based and multiscale modeling, and pattern recognition. The project will train a postdoctoral scholar, PhD student, and six undergraduate researchers through involvement in the research.\n\nThis project brings together several different focused problems with large, multimodal, complex datasets. The data is organized into a knowledge graph in which additional information is added and absorbed as it becomes available. This project considers three types of knowledge graphs each for different applications: (1) knowledge graphs constructed from complex multi-part narratives; (2) knowledge graphs constructed from heterogeneous online content; and (3) knowledge graphs associated with large-scale human interaction dynamics such as a global pandemic. For (1), algorithms will be designed to identify important causal subgraphs. For (2), the project aims to identify threats in space and time based on templated patterns. For (3), desired goals are both a predictive ability for actions from a micro to macro scale along with tools to assess potential impact versus cost of preventative measures, from local to regional to country-wide scale.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2558", "attributes": { "award_id": "2002313", "title": "Non-Markovian Diffusion Imaging", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "Chemical Measurement & Imaging" ], "program_reference_codes": [], "program_officials": [ { "id": 7356, "first_name": "Kelsey", "last_name": "Cook", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-08-31", "award_amount": 399932, "principal_investigator": { "id": 7357, "first_name": "Louis", "last_name": "Bouchard", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "With support from the Chemical Measurement & Imaging program and co-funding from the Atomic, Molecular, and Optical Physics - Experiment and Theory programs, Professor Bouchard at the University of California, Los Angeles is developing new methods to study gaseous diffusion - the random motions of molecules in gases. In traditional models of these processes, a snapshot (state) at any given moment is assumed to be independent of past collisions between molecules swimming in the gas. However, if the picture can be taken quickly enough, it is possible to detect molecules’ \"memory\" of collisions from the recent past, and thus to investigate the nature of surfaces encountered by the molecules. Dr. Bouchard's group is developing both experimental methods and the underlying theory to advance understanding in systems (e.g., catalysts and lungs) where the memory of these collisions are important. They use nuclear magnetic resonance (NMR) spectroscopy, the tool at the heart of Magnetic Resonance Imaging (MRI, an important medical diagnostic tool) which avoids the use of harmful ionizing radiation like x-rays. Graduate and undergraduate students engaged in this research receive broad interdisciplinary training, and contribute to the development of relevant introductory chemistry materials that will be made freely available to the public. \n\nSelf-diffusion in a dense gas of neutral molecules is non-Markovian and must be modeled by a Langevin equation with memory kernel. The Bouchard group has obtained experimental NMR spectroscopy results that are consistent with a generalized Langevin description, as confirmed by an unexpected temperature dependence of the NMR linewidth as well as dependence on inter-pulse spacing during Carr-Purcell-Meiboom-Gill (CPMG) experiments. While the new theory describes the results of free self-diffusion well, the diffusion behavior in the presence of boundaries has not yet been explored. Dr. Bouchard is now probing restricted diffusion in porous media possessing various types of boundaries, and developing new extensions of the underlying theory based on the stochastic calculus of bounded diffusions (sticky, reflecting, killing, and absorbing boundaries). The new NMR-based methods may shed new light into the kinetic theory of gases, benefitting chemical physics and medical imaging by offering new tools to extract information about pore structure and function in media such as porous rocks, soils, or lungs. The tools also show promise for both characterization of catalytically reactive surfaces and mass transport during reactions and provision of a better understanding of hyperpolarized gas MRI. Educational impacts will derive from the training and active participation of multiple students in the research as well as the creation of free online educational course materials disseminated via the institution's web site.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "2938", "attributes": { "award_id": "1918564", "title": "Doctoral Dissertation Research: Regionally-Led Processes and the Building of Civil Society", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Cult Anthro DDRI" ], "program_reference_codes": [], "program_officials": [ { "id": 8896, "first_name": "Jeffrey", "last_name": "Mantz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-09-15", "end_date": "2021-08-31", "award_amount": 22554, "principal_investigator": { "id": 8899, "first_name": "Hannah", "last_name": "Appel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 8898, "first_name": "Zachary", "last_name": "Mondesire", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "What role do regional bodies have in the building and maintenance of civil society? This project, which trains a graduate student in methods of rigorous, empirical data collection and analysis, explores how notions of political belonging are formed and sustained between nation and region. In particular, it will explore new forms of political belonging and governance that exceed national borders, asking what can be learned from supra-national regional dynamics, processes, and imaginations that are becoming increasingly significant in contemporary global politics. In an era when regional alliances and strategies, from the European Union to the North American Free Trade Agreement, are increasingly the subject of intense debate, the significance of region-building more broadly will enhance our understanding of the comparative geopolitics of regional bodies and their role in conflict prevention and resolution. In addition, the project would promote scientific understanding by broadly disseminating its findings to organizations invested in formulating effective practices for the building of civil society.\n\nThe research will be carried out by University of California, Los Angeles doctoral student, Zachary Mondesire, under the supervision of Dr. Hannah Appel. Through a 12-month multi-sited ethnographic study with South Sudanese political and intellectual figures living and working in Khartoum and Nairobi, the project will closely examine the complex ways that individuals understand the political, racial, and geographic communities of which they are a part. To better understand how regional ideas of belonging take shape, he will investigate South Sudan's decision to join the East African Community (EAC), an intergovernmental organization that includes Kenya, Tanzania, Uganda, Burundi, and Rwanda. He will also trace the ways the South Sudan's ongoing relationship with Sudan continues to shape conflict resolution and mediation in local politics. The participants of this study will include South Sudanese journalists, students, professors, and political figures. Data will be collected through interviews, participant observation, media content analysis, and the analysis of memoirs of South Sudanese political leaders. The findings of this study will lead to more robust understandings of the political, economic, and cultural dimensions of regionally-led processes.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "3177", "attributes": { "award_id": "1832544", "title": "NSF Student Support for the 2018 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Info Integration & Informatics" ], "program_reference_codes": [], "program_officials": [ { "id": 9936, "first_name": "Sylvia", "last_name": "Spengler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2018-05-01", "end_date": "2020-04-30", "award_amount": 25000, "principal_investigator": { "id": 9937, "first_name": "Wei", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This grant provides support for 25 U.S.-based graduate students to participate in the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018), to be held in London, United Kingdom August 19-23, 2018. KDD is the world's premier conference in data science. It is a prestigious interdisciplinary conference that brings together researchers and practitioners every year, over 2,000 in recent years, from all aspects of data science, data mining, and knowledge discovery. A strong representation of U.S. students and researchers is essential in maintaining U.S. competitiveness in this important area today and into the future. Since 2013, the KDD conference includes a Broadening Participation in Data Mining workshop. As in various STEM fields, increasing the diversity of participants in KDD research areas is a primary goal. The grant will provide travel support for 25 students. The selection committee will select recipients of the support based on merit and need and place particular emphasis on diversity in the selection process.\n \nThe KDD technical program includes oral and poster presentations of refereed papers, panel discussions, and invited talks by the world's leading experts. It provides an international forum for presentation of original research results as well as exchange and dissemination of innovative and practical development experiences, which are particularly valuable for student participants. Besides the technical program, the conference features workshops, tutorials, panels, posters, project showcases, a data mining contest (KDD Cup), social networks and job matching sessions, and a PhD forum. These will provide a comprehensive multi-facet learning experience to students at all levels. Information on KDD 2018 can be accessed at http://www.kdd.org/kdd2018/. Proceedings will be made available through the ACM Digital Library.\n\nThis 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.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1406, "pages": 1424, "count": 14236 } } }