Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1405&sort=-awardee_organization
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-awardee_organization", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-awardee_organization", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1406&sort=-awardee_organization", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1404&sort=-awardee_organization" }, "data": [ { "type": "Grant", "id": "350", "attributes": { "award_id": "2152774", "title": "Accelerating Bayesian Dimension Reduction for Dynamic Network Data with Many Observations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 626, "first_name": "Yulia", "last_name": "Gel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] } ], "start_date": "2022-07-01", "end_date": "2025-06-30", "award_amount": 300000, "principal_investigator": { "id": 627, "first_name": "Andrew", "last_name": "Holbrook", "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": "Global viral epidemics produce vast amounts of high-dimensional spatiotemporal data. Scientists, businesses, governments and independent organizations want to learn from this data so they can understand basic biological mechanisms, invest capital, allocate aid and design coherent policy in a changing world. Analyzing spatial associations within viral contagion is, unsurprisingly, an area of immense scientific interest, but the task requires accounting for the dynamic and multiscale transportation networks that shape the global economy. This project seeks to advance knowledge of statistical inference from stochastic process models in the context of massive amounts of dynamic and network-indexed data. The proposed research ideas will avoid costly direct representations of network structure and instead use Bayesian dimension reduction to probabilistically map network dynamics to a continuous domain. The project combines theoretical and methodological developments in scalable Bayesian dimension reduction; develops efficient algorithms into open-source, high performance computing (HPC) software; and applies them to the high-impact analysis of viruses including, but not limited to, SARS-CoV-2. The project will emphasize the combination of rigorous statistical methodology with parallel computing techniques available to any scientist with moderate resources.The project will combine theory, methods and applications in advancing knowledge of statistical inference for network-indexed processes. Bayesian multidimensional scaling (BMDS) stands as an established tool for probabilistic dimension reduction of network data but the method's quadratic computational complexity prohibits big data application. The project will extend BMDS to the analysis of millions of data points using a multipronged approach. From a theoretical standpoint, the investigators will show that the classical BMDS model is strictly equivalent to a modified BMDS model with sparse couplings between observations. This 'free lunch' result will amount to a linear reduction in the computational complexity of the classical algorithm, but its use will require an upper bound on the rank of the traditional BMDS distance matrix. A jointly methodological and theoretical investigation will develop a cutting-edge rank estimation procedure for Euclidean distance matrices (EDM) and derive non-asymptotic and asymptotic bounds for the rank estimation error and its impact on the modified BMDS posterior. Bayesian inference with the developed sparse BMDS (S-BMDS) will amount to simulating a massive N-body problem with sparse pairwise couplings. A primary methodological investigation will develop fast parallel algorithms for computing (1) the S-BMDS likelihood and gradient, and (2) the EDM rank in ways that efficiently use multi-core and vectorized central processing units (CPU) and multiple graphics processing units (GPU). The investigators will then allow trends in Google mobility data to inform effective distances between viruses and use our developed machinery to model the spread of, e.g., SARS-CoV-2 through global mobility space. The project also includes an expansive plan for educational, outreach and mentoring activities and will actively disseminate the research findings in a form of open-source HPC software.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": "583", "attributes": { "award_id": "2049670", "title": "Workshop: Aligning AI and U.S. Advanced Manufacturing Competitiveness", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1265, "first_name": "Bruce", "last_name": "Kramer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2021-08-31", "award_amount": 98280, "principal_investigator": { "id": 1266, "first_name": "James F", "last_name": "Davis", "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": "The Workshop on Aligning AI and U.S. Advanced Manufacturing Competitiveness will convene the advanced manufacturing research and practitioner communities, relevant companies, federal agencies and national laboratories and the Manufacturing USA Institutes and experts in artificial intelligence (AI) and machine learning (ML) applications, information technology, and computer science to comprehensively assess the role of AI in manufacturing competitiveness. The workshop, held under the auspices of the Subcommittee on Advanced Manufacturing and the Subcommittee on Machine Learning and Artificial Intelligence of the National Science and Technology Council, seizes on a moment of opportunity that results from the converging effects of the accelerating digital transformation of the global manufacturing industry, major global competitiveness drivers for both advanced manufacturing and AI/ML, and the sustained impacts of the COVID-19 pandemic on manufacturing supply chains. When taken together, there emerges a need to: accelerate a U.S. manufacturing consensus on resilient manufacturing as a priority, address how the digital transformation of manufacturing shapes and is shaped by that transformation, and focus national attention on AI/ML technologies in the context of the nation’s manufacturing priorities. The workshop will be a professionally-managed, virtual teleconference that will emphasize the following manufacturing priorities related to digitalization: manufacturing ecosystem and supply chain restructuring, connectedness, visibility, interoperability, and agility in preparing for and responding to global and national disruptions; greater performance and precision in advanced process and machine operations as key assets in resilient manufacturing ecosystems; a safe and healthy, broadly-skilled, and data-savvy workforce that can be more flexibly deployed; and industry data flow and exchange, cyber opportunity, and national cyber and data security. The workshop will generate a cross-stakeholder consensus on what roles AI has and can have in improving U.S. manufacturing competitiveness and promoting collaboration by the advanced manufacturing and AI/ML research communities in formulating a national strategy that encompasses the workforce, technology and implementation.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": "611", "attributes": { "award_id": "2027190", "title": "Collaborative Research: NSFGEO-NERC:Conjugate Experiment to Investigate Sources of High-Latitude Magnetic Perturbations in Coupled Solar Wind-Magnetosphere-Ionosphere-Ground System", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1355, "first_name": "Lisa", "last_name": "Winter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-10-01", "end_date": "2024-09-30", "award_amount": 238875, "principal_investigator": { "id": 1356, "first_name": "James M", "last_name": "Weygand", "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 is a project that is jointly funded by the National Science Foundation’s Directorate of Geosciences (NSF/GEO) and the National Environment Research Council (UKRI/NERC) of the United Kingdom (UK) via the NSF/GEO-NERC Lead Agency Agreement. This Agreement allows a single joint US/UK proposal to be submitted and peer-reviewed by the Agency whose investigator has the largest proportion of the budget. Upon successful joint determination of an award, each Agency funds the proportion of the budget and the investigators associated with its own investigators and component of the work. This project is to (1) operate, maintain, and expand a high-latitude array of autonomous instruments to support research of the wider geospace research community into the sources of inter-hemispheric asymmetries, (2) conduct focused science investigations to develop understanding of the sources of high-latitude magnetic perturbations in the multi-scale, global, solar wind - magnetosphere – ionosphere – ground (SWMIG) system, including during the 2021 solar eclipse and (3) conduct education and outreach to facilitate broader access to polar research efforts. These objectives will be achieved through an unsurpassed network of closely-spaced magnetically-conjugate magnetometers in Antarctica and in the Northern Hemisphere near the 40 degree magnetic meridian, most of which have already been deployed. This project expands an existing Virginia Tech/Technical University of Denmark partnership to include the British Antarctic Survey (BAS), Space Science Institute, and UCLA. Graduate and undergraduate students will be supported, including a special research program to engage students from minority-serving institutions.Measurements of surface magnetic field perturbations are important to remotely sense and characterize the SWMIG phenomena that affect technology – such as geomagnetically induced currents – and thereby to develop physical models and forecast space weather impacts. However, understanding the sources of magnetic perturbations in the coupled SWMIG system is challenging due to their simultaneous dependence on driving conditions, ionospheric conductivity and ground conductivity. We seek to address the following science questions, \"How do magnetosphere-ionosphere current systems couple to high-latitude ground magnetic perturbations? What roles do current system spatial scale, inhomogeneous ionospheric conductivity, and inhomogeneous ground conductivity play?\" By combining British Antarctic Survey, Technical University of Denmark, and NSF-supported magnetometers, a new combined array will provide unprecedented coverage throughout the auroral zone/cusp in both hemispheres simultaneously. These data enable novel experiments to isolate the respective contributions of driver spatial/temporal scale, ionospheric conductivity, and local ground conductivity in the generation of ground magnetic perturbations. This project includes field work in the Antarctic, supported by both the U.S. Antarctic Program (USAP) and the BAS. USAP and BAS have agreed to support maintenance visits to receiver site locations and to support the retrograde of equipment at the end of the program. BAS and USAP will work collaboratively to deploy an additional instrument to a logistically feasible location that best serves the project. The USAP and BAS have agreed to support this program logistically, with the first field deployment year to be determined after the uncertainties related to the coronavirus pandemic are resolved.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": "638", "attributes": { "award_id": "2039800", "title": "Experiences that Shape Undergraduate Computing Trajectories: A Equity-Focused Longitudinal Study at Center for Inclusive Computing Institutions", "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": [], "program_officials": [ { "id": 1448, "first_name": "Jeffrey", "last_name": "Forbes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-10-01", "end_date": "2023-09-30", "award_amount": 1640400, "principal_investigator": { "id": 1450, "first_name": "Linda J", "last_name": "Sax", "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": 1449, "first_name": "Kathleen J", "last_name": "Lehman", "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": "The Momentum: Accelerating Equity in Computing and Technology research program at the University of California, Los Angeles will conduct a study to produce knowledge that can guide departments’ decisions about recruitment and retention in computing, especially with regard to ensuring equitable structures and opportunities for students who have been historically minoritized. While responding to workforce demands for trained computer scientists, undergraduate computing programs are also increasing their focus on cultivating opportunities for participation among women and students of color (specifically, students who identify as Black or African American, Hispanic or Latinx, Native American, Native Hawaiian or Pacific Islander). The urgent need to address equity issues in undergraduate computing departments is more salient now than ever before, as U.S. colleges and universities currently face a turbulent road to navigate operations amid and after the coronavirus pandemic. This study will provide timely data that can inform undergraduate computing’s recruitment and retention efforts in the current contexts. In collaboration with Northeastern University’s Center for Inclusive Computing (CIC), the Momentum team will survey first- and second-year students who enroll in computing courses at approximately 20 colleges and universities in the spring of 2021. This survey will ask a variety of questions ranging from students’ backgrounds and precollege experiences to their computing experiences in the 2020-21 academic year. Then, they will survey these students again in the spring of 2023 to assess how their experiences with computing have changed and the role these experiences play in shaping their future plans. The project team will analyze these data to understand the specific experiences that shape students’ trajectories in computing, especially the pathways of women and students from historically minoritized groups. The findings from this study will be disseminated widely within the computing and higher education community to inform best practices to recruit and retain students in computing majors and ultimately into computing careers.The Momentum research program at the University of California, Los Angeles will conduct a longitudinal study of first- and second-year students who enroll in computing courses at institutions involved in Northeastern University’s Center for Inclusive Computing (CIC). The research represents a partnership between Momentum and CIC to engage in research on collegiate experiences that promote recruitment and retention in computing, particularly for marginalized students in computing such as women of color. Leveraging Momentum staff expertise and survey instruments to address key gaps in our knowledge, the project team will first administer a baseline survey to first- and second-year students enrolled in computing courses at CIC institutions. This survey (administered in spring 2021) will focus on a variety of factors, ranging from students’ background characteristics and pre-college experiences to their transition to college and their first-year experiences in computing. Then, two years later, in students’ third or fourth year in college, the project team will administer a follow-up survey. The follow-up survey will focus on the specific experiences known to shape students’ trajectories in computing, including course experiences, interactions with instructors/faculty, and extracurricular computing experiences (e.g., involvement in computing organizations, undergraduate research, computing-related internships). This survey will also focus on students’ longer-term plans, such as pursuing graduate school or careers in computing. This study will contribute to knowledge about broadening participation in computing in several ways. First, it will provide insights to scholars, administrators, and policymakers about how students who take computing courses in the first- and second- year of college may engage with the computing department and how this engagement may differentially shape the trajectories of students from various gender and/or racial/ethnic groups. Additionally, following up with these students longitudinally will enable us to learn how these students’ experiences and perceptions change over-time, as well as to study the longer-term role played by a variety of computing environments and outcomes. Overall, this study aims to build on existing literature about what works to promote desirable outcomes for computing students and provide more data on how and why certain experiences work (or do not work), thereby providing actionable findings to stakeholders designing interventions to promote broadening participation efforts in computing.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": "834", "attributes": { "award_id": "2054253", "title": "DMS/NIGMS 2: Statistical Methods and Computational Algorithms for Biobank Data", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 1989, "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": "2021-07-01", "end_date": "2025-06-30", "award_amount": 481762, "principal_investigator": { "id": 1991, "first_name": "Hua", "last_name": "Zhou", "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": 1990, "first_name": "Jin", "last_name": "Zhou", "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": "Biobank data is characterized by its volume, velocity, variety, and veracity (4V). Two prime examples are the Million Veteran Project (MVP) at US Veterans Affairs (VA) and UK Biobank. The data are big, with up to a million subjects and occupying terabytes of storage (volume). Their sample sizes and data content keep increasing (velocity). They contain heterogeneous sources of information: genome, electronic health record (EHR), wearable devices, images, and most recently, COVID-19 data (variety). Furthermore, they are fraught with missingness and inaccuracy (veracity). This project seeks to develop novel statistical methods and computational algorithms that address specific aspects of 4V. The methods are motivated by the principal investigators' recent experience in analyzing MVP and UK Biobank data, and are generalizable to any biobank or other generic big data. The methods provide solutions to some of the most pressing issues in biobank data analysis. The work will push forward several frontiers in statistics, optimization, and genetics. The research will be integrated with substantial education and outreach activities, including developing new courses and software and mentoring students. These activities aim to expose a diverse set of students, including women and minorities, to state-of-the-art statistical and computational techniques for big data analysis.Three sets of problems are to be investigated. (1) Electronic health records and wearable devices generate a vast amount of longitudinal data in biobanks. In many studies, the within-subject variability of a longitudinal outcome is the primary scientific interest. Motivated by studies of the impacts of blood pressure variability and glycemic variability on diabetes complications, the PIs propose a robust and scalable method for the estimation and inference of the effects of both time-varying and time-invariant predictors on within-subject variance. Compared to existing approaches, the method is robust to the distribution misspecification and orders of magnitude faster. Computational scalability makes it a powerful tool for studying trait variability based on massive longitudinal data in biobanks. (2) The PIs will develop a new class of online learning algorithms, which combine the majorization-minimization principle in statistics and the stochastic proximal iteration algorithm. The new algorithms apply to a broader class of models and are demonstrably more stable and robust. They help solve the volume issue and will be applied to genome-wide association studies of massive biobank data. (3) The PIs propose a bag of little bootstraps (BLB) approach for estimating massive variance component models, which play a central role in genetics and biostatistics. Fitting such models is prohibitive for biobank data because of the inversion of the giant covariance matrix. The BLB approach breaks the massive variance component model into many smaller ones, which are bootstrapped in parallel and then averaged. The new method will enable quantifying heritability and genetic correlation of complex traits in biobank 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "905", "attributes": { "award_id": "2125664", "title": "EAGER: Understanding the genomes and strain mutations of SARS-CoV-2 and other viruses using comparative and population genetic approaches", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 2171, "first_name": "Peter", "last_name": "McCartney", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-05-01", "end_date": "2023-04-30", "award_amount": 300000, "principal_investigator": { "id": 2174, "first_name": "Jason", "last_name": "Ernst", "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": 2172, "first_name": "Sriram", "last_name": "Sankararaman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2173, "first_name": "Noah", "last_name": "Zaitlen", "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": "Comparative genomics provides a powerful lens in which to understand novel genomes. This EAGER award sees to gain a deeper scientific understanding of the genomes and strain mutations of SARS-CoV-2 and other viruses by developing and applying innovative comparative genomic and population genetic methods leveraging the expertise of the investigators in these areas. These analyses will elucidate the relative contributions of evolutionary forces that shape the SARS-CoV-2 genome and other virus genomes across deep and recent evolutionary time scales and will pinpoint functionally important positions in their genomes. They will inform efforts of the broader scientific community to model the dynamics of infection, strategies for mitigation, and to develop therapeutics. This research will be integrated into educational programs that leverage the BIG summer undergraduate research program and the Computational Genomics Summer Institute at UCLA.The specific aims are to develop and apply comparative genomic analyses for SARS-CoV-2 using multiple genome alignments of various coronavirus genomes. The work will leverage the recently developed ConsHMM method to annotate the SARS-CoV-2 genome at single nucleotide resolution into different conservation states. ConsHMM defines conservation states based on the combinatorial and spatial patterns of which species align to and match a reference genome using a multivariate hidden Markov model (HMM). These conservation states will be related to other annotations of the SARS-CoV-2 genome including (inter)genic and key protein regions. Population genetic approaches will be applied to publicly available sequences of the SARS-Cov-2 genome to infer fundamental properties of mutations. To estimate these parameters, novel structured coalescent-based models will be developed that account for demographic processes across multiple time points and locations. While the initial investigation will focus on the large collections of SARS-CoV-2 genomes that are becoming available, the methods developed will be applicable to the study of viral genomes (such as other coronaviruses, influenza, ebola, and HIV) and their strain mutations.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": "983", "attributes": { "award_id": "2103700", "title": "Packaging, Targeting, and Replication of Virus-derived RNA Replicons", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 2390, "first_name": "Clifford", "last_name": "Weil", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-07-01", "end_date": "2024-06-30", "award_amount": 763871, "principal_investigator": { "id": 2391, "first_name": "William M", "last_name": "Gelbart", "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 order to better control and treat viral outbreaks such as the current COVID-19 pandemic, it is necessary to better understand on a molecular level the life cycles of RNA viruses – how they get into and out of their host cells, and how they replicate in those cells. This project focuses on three of the simplest and best characterized RNA viruses – ones, like SARS-2, whose genomes are single-stranded RNA that the target cell translates into viral proteins. This project aims to discover fundamental aspects of how a single RNA genome is amplified 10,000-fold within hours, how the new RNA molecules are packaged into protective protein shells (capsids), and how these newly-formed particles (nucleocapsids) exit their host cell. The research involved will be carried out by diverse undergraduate, graduate, and postdoctoral students who are being trained in state-of-the-art molecular biology, chemistry and physics methods, including genetic engineering, biochemical/enzymatic reactions, and fluorescence and electron microscopies. The results of this work will provide foundational information that can propel vaccine design, antiviral pharmaceutical discovery, and innovations in biotechnology. The three viruses featured in this research are: a bromovirus whose four genes are contained in three RNA genome molecules packaged in three different particles; a nodavirus whose four genes are contained in two molecules packaged together in one particle; and an alphavirus whose nine genes are all contained in one RNA molecule and one particle. These viruses illustrate the breadth of strategies for replication of and packaging of RNA genes into virus particles. Cells recognize these viral genomes as if they were mRNAs so that the genomes are all self-replicating in the sense that they encode RNA replicase proteins that replicate the genome. In comparing and contrasting the life cycles of these viruses the ultimate goal is to control the self-assembly of self-replicating RNA molecules. The state-of-the-art physical and molecular biological techniques involved include: time-resolved cryo-electron tomography; genetic engineering of capsid-forming proteins; and single-molecule/single-cell fluorescence microscopy. The particular experiments include: time-resolved tomographic imaging of the self-assembly pathway of RNA viruses and VLPs; syntheses of in vitro reconstituted VLPs functionalized by protein ligands; and competitions of viral and non-viral RNA molecules for RNA replicases.This research is funded by the Genetic Mechanisms program in the Division of Molecular and Cellular Biosciences in the Directorate of Biological Sciences.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": "1065", "attributes": { "award_id": "2106859", "title": "III: Medium: Collaborative Research: Collaborative Machine-Learning-Centric Data Analytics at Scale", "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": [], "program_officials": [ { "id": 2630, "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": "2021-10-01", "end_date": "2024-09-30", "award_amount": 156032, "principal_investigator": { "id": 2631, "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": [ { "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 recent years our society has enjoyed the huge value of online collaboration and sharing, as evidenced by popular cloud-based services such as Google Docs, Dropbox, GitHub, and Overleaf. These benefits become even more attractive due to the new norm of working remotely caused by the unprecedented Covid-19 pandemic. In this award the investigators want to answer the following question: is it possible to develop online systems to support cloud-based services for collaborative data analytics? This computing paradigm allows collaborators to jointly conduct an analysis job on a large amount of data. The investigator team is particularly interested in scenarios where collaborators are from multiple disciplines with different backgrounds, and the analytics is machine learning centric, since such tasks are becoming increasingly common and important. While collaborators in data analytics want to focus on their research topics and fully utilize their expertise and skills, they are also facing challenges due to their complementary backgrounds and asynchronous working schedules. As a consequence, the collaboration has both inter-disciplinary obstacles and intra-disciplinary obstacles. The goal of this award is to study these challenges and develop new techniques to support such novel online services to support collaborative data analytics. The investigator team identifies four unique research topics: 1) Allowing collaborators to debug the training process of a machine learning model by pausing and resuming the process or setting conditional breakpoints, as these tasks tend to be computationally intensive; 2) Enabling collaborative debugging of external user-defined functions in order to not only harness the popular data science libraries in Python and R, but also achieve a high performance using a parallel data-processing engine often written in other languages such as Java and Scala; 3) Supporting collaborative instance labeling and machine learning training and deployment between domain scientists and machine learning experts; and 4) Analyzing and mining the collected data workflows from collaborators to improve the user productivity to formulate new data analytics tasks. The developed techniques will bring the success of many cloud-based collaboration services to the increasingly important space of scalable data analytics using machine learning techniques. The solutions will significantly lower the barriers to entry in terms of enabling domain-specific analysts -- as opposed to computer-science-trained Big Data experts -- to gather and to efficiently, effectively, and interactively analyze large quantities of data in different domains.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": "1526", "attributes": { "award_id": "2035890", "title": "RAPID: Learning about Coronavirus Genome Replication by Interfering with It", "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": 3981, "first_name": "Clifford", "last_name": "Weil", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2022-06-30", "award_amount": 200000, "principal_investigator": { "id": 3983, "first_name": "William M", "last_name": "Gelbart", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 3982, "first_name": "John", "last_name": "Mellnik", "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": "Due to the global pandemic caused by SARS-CoV-2, there is an urgent need to understand how the virus reproduces. After the virus infects a cell, the viral replicase enzyme makes thousands of copies of the viral genome. This project will determine the molecular features of the SARS-CoV-2 RNA genome that enable it to be copied by the coronavirus replicase complex. Having determined those features, the PIs will use that knowledge to design genome-like molecules that may interfere with replication of normal genomes and thus have potential as novel therapeutic agents. The PIs will evaluate the effectiveness of the genome-like molecules at blocking virus replication in cells and in more complicated tissue culture that includes a variety of different cell types meant to mimic the conditions in the human lung. Additionally, the project will broaden participation in STEM by funding a Latina post-doctoral scholar to perform most of the experiments.The PIs will use a combination of biochemistry and culture to determine the minimal features of a SARS-CoV-2 replicon that enable it to be replicated. Next, the PIs will apply that knowledge to design defective-interfering RNA that can be replicated very efficiently yet does not encode infectious virus particles. Extending from natural defective-interfering particles in other viruses, this RNA will likely serve as a template for the coronavirus replicase. When an intact virus penetrates a cell that contains defective-interfering RNA, the defective interfering RNA will serve as a template for the virus replicase. Doing so will interfere with viral replication by occupying the replicase and consuming nucleotides that could otherwise be incorporated into new intact virus genomes. The interfering RNAs will be delivered using a virus-like particle system designed to enter cells expressing the SARS-CoV-2 receptor and the protease necessary for SARS-CoV-2 entry. The PIs will determine the extent to which defective-interfering particles block replication in simple tissue culture or in human airway epithelial organoids, which have multiple differentiated cell types iorganized similarly to those found in explanted tissue. The epithelial organoids are derived from healthy human tissue donors and represent some of the genetic diversity in human populations. Broader impacts include the potential for basic understanding of the interfering particles to contribute to developing similar particles to treat or prevent COVID-19.This RAPID award is made by the Genetic Mechanisms Program in the Division of Molecular and Cellular Biosciences to respond to the COVID-19 pandemic.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": "1591", "attributes": { "award_id": "2034234", "title": "EAGER: High-throughput early detection and analysis of COVID-19 plaque formation using time-lapse coherent imaging and deep learning", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7916" ], "program_officials": [ { "id": 4170, "first_name": "Steve", "last_name": "Zehnder", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-15", "end_date": "2022-08-31", "award_amount": 299947, "principal_investigator": { "id": 4172, "first_name": "Aydogan", "last_name": "Ozcan", "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": 4171, "first_name": "Hatice Ceylan", "last_name": "Koydemir", "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": "Plaque assays are widely used for measuring the infectious concentration of viral samples and form a very important tool for vaccine development, especially for the evaluation of the performance of new vaccines at the exploratory and preclinical stages. This standard method is laborious and takes days to get the results, and is subject to human errors since it depends on manual plaque counting. Molecular techniques such as polymerase chain reaction (PCR or reverse transcription PCR) and western blots can be used to quantify the viral genome. However, none of these methods provide information about the infectivity of the virus and cannot measure plaque forming units. This proposal aims to create a computational sensor platform for accelerated testing of SARSCoV-2 viability and infectivity using deep learning-based plaque assays and achieve accurate and automated plaque forming unit (PFU) measurements within hours as opposed to days with standard plaque assays. The proposed computational imaging system will periodically capture coherent microscopic images of the cytopathogenic effects of viruses on cell cultures and analyze these time lapsed holographic images using deep neural networks (DNNs) for rapid detection of viral destruction of the cell monolayer. In addition to early and automated detection of plaque forming units, this unique platform will further make use of deep learning for high-throughput holographic image reconstruction of the assay volume to perform tile-scan imaging of the entire well plate within 5 min, corresponding to an imaging throughput of ~50 cm2/min. Powered by deep learning, this automated and cost-effective viral plaque monitoring platform can be transformative for a wide range of applications in microbiology and virology by significantly reducing the detection time without labeling or the need for an expert, or manual inspection. The project will also establish a complementary educational outreach program that will involve (1) public interviews and popular science articles in news media and internet; (2) undergraduate research opportunities in the PI’s laboratory involving minority students; and (3) graduate student training through organization of workshops, seminars and conferences. Furthermore, research projects, seminars and open house visits will serve undergrads and high school students (especially from minority groups) to interact with a cutting edge research environment, helping to increase their scientific curiosity and shaping their career goals in science and engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1405, "pages": 1424, "count": 14236 } } }