Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1386&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1397&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1387&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=-keywords" }, "data": [ { "type": "Grant", "id": "10109", "attributes": { "award_id": "2114651", "title": "Collaborative Research: A Workshop on Pre-emergence and the Predictions of Rare Events in Multiscale, Complex, Dynamical Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Systematics & Biodiversity Sci" ], "program_reference_codes": [], "program_officials": [ { "id": 923, "first_name": "Katharina", "last_name": "Dittmar", "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": 81834, "principal_investigator": { "id": 25749, "first_name": "Nina H", "last_name": "Fefferman", "orcid": "https://orcid.org/0000-0003-0233-1404", "emails": "[email protected]", "private_emails": "", "keywords": "[]", "approved": true, "websites": "['https://www.medrxiv.org/']", "desired_collaboration": "", "comments": "", "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 190, "ror": "", "name": "University of Tennessee Knoxville", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "Although pandemics have threatened human civilization since ancient times, how to predict and prevent them remains one of the most pressing challenges, calling out for innovative insights and practices. Pandemics emerge through incidental ‘perfect storms’: molecular changes in pathogens, gradual trends in climate, subtle shifts in ecological interactions among potential hosts, and even individual behavioral decisions by people, all colluding to make up the difference between an interesting but rare new variant of a known disease and an existential worldwide crisis. Being able to predict the emergence of pandemic threats, therefore, requires a fully integrated, multidisciplinary approach, able to consider the complexity of these realms across scales of interaction to predict and, ideally, prevent. This workshop will include experts from otherwise disparate scholarly communities in biology, mathematics, engineering, computer science, ecology and social science to come together and discuss how to integrate the approaches taken by each community into a more effective, unified science of pandemic prediction. Discussions will leverage recently developed advances in disease ecology, computational biology and biophysics, information and network science, sensing, and statistics to analyze pertinent data, enabling inference of difficult-to-measure information and the integration of real-time observation, computation, and experimentation. \n\nThe workshop aims at formulating a new science base on pandemic preparedness, identifying scientific gaps that need to be addressed, and discussing how to design solutions to fill those gaps in ways that anticipate multidisciplinary use. Participants will consider how to construct integrative and multidisciplinary frameworks to enable better insights into the fundamental processes of pandemic emergence and translate those insights into practical tools for preventing and/or mitigating pandemic threats. It is anticipated that the workshop will result into concrete recommendations for how the critical and diverse relevant fields can move forward together to increase global safety, guarding against future pandemics.\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": "10110", "attributes": { "award_id": "2114503", "title": "Collaborative Research: A Workshop on Pre-emergence and the Predictions of Rare Events in Multiscale, Complex, Dynamical Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Systematics & Biodiversity Sci" ], "program_reference_codes": [], "program_officials": [ { "id": 923, "first_name": "Katharina", "last_name": "Dittmar", "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": 10000, "principal_investigator": { "id": 24641, "first_name": "James", "last_name": "Moody", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Although pandemics have threatened human civilization since ancient times, how to predict and prevent them remains one of the most pressing challenges, calling out for innovative insights and practices. Pandemics emerge through incidental ‘perfect storms’: molecular changes in pathogens, gradual trends in climate, subtle shifts in ecological interactions among potential hosts, and even individual behavioral decisions by people, all colluding to make up the difference between an interesting but rare new variant of a known disease and an existential worldwide crisis. Being able to predict the emergence of pandemic threats, therefore, requires a fully integrated, multidisciplinary approach, able to consider the complexity of these realms across scales of interaction to predict and, ideally, prevent. This workshop will include experts from otherwise disparate scholarly communities in biology, mathematics, engineering, computer science, ecology and social science to come together and discuss how to integrate the approaches taken by each community into a more effective, unified science of pandemic prediction. Discussions will leverage recently developed advances in disease ecology, computational biology and biophysics, information and network science, sensing, and statistics to analyze pertinent data, enabling inference of difficult-to-measure information and the integration of real-time observation, computation, and experimentation. \n\nThe workshop aims at formulating a new science base on pandemic preparedness, identifying scientific gaps that need to be addressed, and discussing how to design solutions to fill those gaps in ways that anticipate multidisciplinary use. Participants will consider how to construct integrative and multidisciplinary frameworks to enable better insights into the fundamental processes of pandemic emergence and translate those insights into practical tools for preventing and/or mitigating pandemic threats. It is anticipated that the workshop will result into concrete recommendations for how the critical and diverse relevant fields can move forward together to increase global safety, guarding against future pandemics.\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": "10111", "attributes": { "award_id": "2049448", "title": "Collaborative Research: Economic Downturns, Global Pandemics and Parliamentary Elections", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "AIB-Acctble Institutions&Behav" ], "program_reference_codes": [], "program_officials": [ { "id": 1532, "first_name": "Lee", "last_name": "Walker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-15", "end_date": "2023-01-31", "award_amount": 191599, "principal_investigator": { "id": 26014, "first_name": "Ora John", "last_name": "Reuter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 587, "ror": "", "name": "University of Wisconsin-Milwaukee", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "Under what conditions do voters withdraw or withhold their support from governments and how do economic and pandemic stressors affect popular support for governments and political leaders? This project advances understanding of how electoral rules, electoral integrity, perceptions of regime popularity and longevity, and the nature of electoral alternatives shape government support. Using surveys timed to Russia’s 2021 parliamentary election and including questions from the Comparative Study of Electoral Systems, this project extends the longest running election study in an autocratic setting and the only covering an extended period of retreat from democracy. The data from this project are an important resource for scholars of authoritarianism, comparative electoral behavior, and political parties, and its findings are also relevant to journalists, policymakers and the public. \n\nExisting scholarship gives good reason to believe that declining popular support can undermine regimes around the world. However, the precise nature of these processes and the long-term developments that lead to regime change remain poorly understood. This study uses a nationally representative panel survey conducted before and after Russia’s 2021 State Duma election to advance knowledge on two of the most consequential forms of political behavior and politically salient metrics of a regime’s popular support: turnout and vote choice. The project’s theoretical contribution focuses on key differences in how voters process information about government performance and assess available political alternatives, with special attention to how polarization, preference falsification, and voters’ emotional states affect their interpretation of new information during crises.\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": "10112", "attributes": { "award_id": "2049595", "title": "Collaborative Research: Economic Downturns, Global Pandemics and Parliamentary Elections", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "AIB-Acctble Institutions&Behav" ], "program_reference_codes": [], "program_officials": [ { "id": 1532, "first_name": "Lee", "last_name": "Walker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-15", "end_date": "2023-01-31", "award_amount": 339122, "principal_investigator": { "id": 26015, "first_name": "Bryn", "last_name": "Rosenfeld", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 279, "ror": "https://ror.org/05bnh6r87", "name": "Cornell University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Under what conditions do voters withdraw or withhold their support from governments and how do economic and pandemic stressors affect popular support for governments and political leaders? This project advances understanding of how electoral rules, electoral integrity, perceptions of regime popularity and longevity, and the nature of electoral alternatives shape government support. Using surveys timed to Russia’s 2021 parliamentary election and including questions from the Comparative Study of Electoral Systems, this project extends the longest running election study in an autocratic setting and the only covering an extended period of retreat from democracy. The data from this project are an important resource for scholars of authoritarianism, comparative electoral behavior, and political parties, and its findings are also relevant to journalists, policymakers and the public. \n\nExisting scholarship gives good reason to believe that declining popular support can undermine regimes around the world. However, the precise nature of these processes and the long-term developments that lead to regime change remain poorly understood. This study uses a nationally representative panel survey conducted before and after Russia’s 2021 State Duma election to advance knowledge on two of the most consequential forms of political behavior and politically salient metrics of a regime’s popular support: turnout and vote choice. The project’s theoretical contribution focuses on key differences in how voters process information about government performance and assess available political alternatives, with special attention to how polarization, preference falsification, and voters’ emotional states affect their interpretation of new information during crises.\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": "10113", "attributes": { "award_id": "2048223", "title": "CAREER: Learning and Leveraging the Structure of Large Graphs: Novel Theory and Algorithms", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Comm & Information Foundations" ], "program_reference_codes": [], "program_officials": [ { "id": 581, "first_name": "Scott", "last_name": "Acton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2026-08-31", "award_amount": 595527, "principal_investigator": { "id": 5808, "first_name": "Gautam", "last_name": "Dasarathy", "orcid": "https://orcid.org/0000-0003-2252-2988", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "From genetic interaction networks and the brain to wireless sensor networks and the power grid, there exist many large, complex interacting systems. Graph theory provides an elegant and powerful mathematical formalism for quantifying and leveraging such interactions. Unsurprisingly, many modern tasks in science and engineering rely on the discovery and exploitation of the structure of graphs. Unfortunately, there is a stark disconnect between the purported capabilities of data-driven algorithms for graph analytics and their real world applicability. Specifically, the following key challenges emerge for existing algorithms: (i) Reliance on large number of expensive experiments/measurements; this is prohibitive in the large systems typically encountered in science and engineering. (ii) Reliance on the availability of curated and labeled datasets; this is untenable outside a narrow set of disciplines. (iii) Design for worst-case scenarios; this lack of adaptivity to structure unique to the problem severely impairs their statistical and computational efficiency. In response to the above challenges, this research program will close the loop on traditional machine learning systems where data acquisition and learning algorithms are designed separately. The project will devise several novel compressive, adaptive, and interactive algorithms that efficiently exploit structure in the problem. These will be complemented by foundational advances to the theory of learning and leveraging structure in graphs. The methodological advances will have impact on diverse areas such as resilient cyber-infrastructure, robust neuroimaging, and intervention design for pandemics. The research activities are tightly integrated with a comprehensive education, mentoring, and outreach plan that will increase awareness, access, and inclusion in STEM, especially with respect to data-driven methods in science and engineering. \n\n\nThe technical contributions of this project are organized into two interrelated themes: (1) Learning the structure of graphs from compressively and interactively acquired data. The research in this theme will reveal new and interesting tradeoffs between the cost of data acquisition and statistical accuracy. These will be complemented by minimax optimal algorithms that achieve various points in the tradespace. (2) Leveraging the graph structure to accomplish efficient inference. The research in this theme is unified by the general problem of level set estimation on graphs and will result in foundational contributions to the theory of nonparametric learning, meta-learning, and sequential decision making. The research themes feature extensive experimental validation, collaboration with domain experts, and translational activities with the view of driving meaningful and long-term impacting on practice.\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": "10114", "attributes": { "award_id": "2046472", "title": "CAREER: A paired experimental-computational approach to elucidate stress responses in early-branching eukaryotes", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Cellular Dynamics and Function" ], "program_reference_codes": [], "program_officials": [ { "id": 3441, "first_name": "Richard", "last_name": "Cyr", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2025-12-31", "award_amount": 1382116, "principal_investigator": { "id": 26016, "first_name": "Jennifer", "last_name": "Guler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 517, "ror": "", "name": "University of Virginia Main Campus", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Cells sense their surroundings and use this information to adapt to stressful conditions. These responses can determine whether a cell dies or survives to change according to the new environment (i.e. different temperature, nutrient levels, or chemical makeup). This project will use a single cell organism to understand more about how cells respond to stress. Specifically, Plasmodium cells survive in a wide range of environments as they live inside or outside host cells and within different animals. These cells use some of the same approaches as multi-cellular animals, yet there are notable differences. Due to their ability to acquire what they need from their host, many parasites streamline their efforts as they evolve; therefore, Plasmodium responses may represent the minimal components required for survival in diverse environments and could provide insight into how other single cell organism adapt to stress. The Broader Impact of the work includes the intrinsic research as this group or organisms infects a wide range of host cells where they can cause such afflictions as malaria. Additional activities seek to improve public literacy by integrating research with an educational outreach program using virtual educational tools, which describe timely biological topics in an accurate and exciting way. This is particularly important during a global pandemic, where a basic understanding of disease transmission can drastically improve adherence to public health guidelines and quality distance learning could diminish the impacts of school closings. \n\nThe research will contribute to the understanding of cellular mechanisms that drive adaptation and survival in response to environmental stress. These responses are critical for early-branching eukaryotic protozoa, such as Apicomplexans, that thrive in a variety of environments during their complex life cycles. Apicomplexan protozoa are expected to harbor the basic components required for a robust stress response due to their early divergence from higher eukaryotes and the physiological buffering provided by their intracellular parasitic lifestyle. Plasmodium, one Apicomplexan genus, retains a heat shock response and translational inhibition, yet nutrient signaling pathways lack key homologues (i.e. the mTOR kinase). This project will bolster the use of computational tools for cross-species comparisons and generate open access multi-omics data sets and mutant parasite lines for use by the research community. Furthermore, identifying aspects of the Apicomplexan stress response that are divergent from higher organisms will challenge prevailing views of the stress response field by highlighting the critical, basic capabilities needed to survive in diverse environments.\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": "10115", "attributes": { "award_id": "2043431", "title": "SCC-CIVIC-PG Track A: Piloting On-Demand Multimodal Transit in Atlanta", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "S&CC: Smart & Connected Commun" ], "program_reference_codes": [], "program_officials": [ { "id": 975, "first_name": "Yueyue", "last_name": "Fan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2021-09-30", "award_amount": 47793, "principal_investigator": { "id": 26020, "first_name": "Pascal", "last_name": "Van Hentenryck", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26017, "first_name": "SUBHRAJIT", "last_name": "GUHATHAKURTA", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26018, "first_name": "Christopher A Le", "last_name": "Dantec", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26019, "first_name": "Kari E", "last_name": "Watkins", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "In the United States, car ownership remains the best predictor of upwards social mobility. Those without a car are grievously disadvantaged in accessing jobs, health care, and decent groceries. Moreover, housing patterns further limit social mobility, as low-income populations often reside far away from job opportunities and have few efficient public transit options. Ride-hailing services have sometimes helped in providing additional mobility options. But, in general, they have widened inequalities in accessibility, servicing the needs of an affluent population, reducing the revenues of transit authorities, and increasing congestion and emissions. The importance of transit has also being highlighted during the pandemic. This award envisions a future for public transit that meets these mobility challenges for all population segments through the concept of On-Demand Multimodal Transit Systems (ODMTS). ODMTS combine on-demand services to serve low-density regions with high-occupancy vehicles (buses and/or trains) to travel along high-density corridors. The resulting door-to-door services have been shown to improve convenience, reduce costs, and provide a unique opportunity to expand services and job accessibility in neighborhoods where traditional transit systems have been too costly.\n\nTo validate the concept of ODMTS at scale, this civic engagement project explores pilots in Atlanta, the city of Smyrna in Cobb county, and Gwinnett county. By studying complementary high-impact pilot settings, i.e., transit deserts, cities with no transit systems, counties in need of better connections to a large city, and support for low-income population, the project hopes to create a blueprint for the deployment of a new generation of transit systems across the country. To support these pilots, this award researches the scientific and technological advances to translate the concept of ODMTS into successful pilots. In particular, it explores four research threads to overcome knowledge gaps: (1) the modeling of mobility patterns and their relationship to the built environment, capturing future housing and retail profiles; (2) the joint optimization of mode adoption and network design; (3) the joint optimization of on-demand and recurrent requests; and (4) the modeling of the transit regulatory environment ((ADA, EEO, Title VI). This award adopts a community-driven participatory design, sustained by advanced simulations, visualizations, and metrics to highlight the potential impact of ODMTS on mobility needs and budgets. The blueprint for deploying ODMTS in cities around the country consists of a software pipeline that covers the data analytics, predictive models, optimization technology, mobile applications, and high-performance computing architecture that plan and operate the transit systems.\n\nThis project is in response to Track A – CIVIC Innovation Challenge - Communities and Mobility a collaboration with NSF and the Department of Energy.\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": "10116", "attributes": { "award_id": "2109583", "title": "Conference on Special Metrics in Complex Geometry", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "GEOMETRIC ANALYSIS" ], "program_reference_codes": [], "program_officials": [ { "id": 6623, "first_name": "Christopher", "last_name": "Stark", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2022-07-31", "award_amount": 21465, "principal_investigator": { "id": 26021, "first_name": "Ronan", "last_name": "Conlon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The Conference on Special Metrics in Complex Geometry will be held at Florida International University from January 7-10, 2021. Complex manifolds are higher-dimensional surfaces that are defined using the complex numbers. Recently, there has been significant progress in the study of \"metrics\" on complex manifolds, objects that endow these spaces with a shape or more precisely, curvature. The study of these metrics lies at the nexus of complex geometry and geometric analysis. The aim of this conference is to gather together experts working in these two fields to discuss these new and exciting developments. Even though lately there have been many conferences in complex geometry, none have really been organised to address these recent discoveries. This conference serves to fulfill this necessity. In doing so, it will promote collaboration across many areas of the aforementioned fields of complex geometry and geometric analysis. The latest breakthroughs in these two fields will be presented to a new generation of mathematicians, with a particular emphasis on women and those from minority groups. The conference will provide a forum for senior mathematicians to interact with graduate students through the organisation of an optional poster presentation for graduate students which will take place in a non-intimidating and relaxed environment. Ample opportunity will also be provided to women speakers to present their research, with at least one talk per day scheduled for a female speaker. Moreover, the conference will provide a platform for postdocs and junior researchers to present their work. Participants will be brought up-to-date with all of the current developments in the field and will be presented with new avenues of research in the enticing environment that Miami provides in the Winter.\n \nMore specifically, the Conference on Special Metrics in Complex Geometry will concentrate on the interplay between complex geometry and geometric analysis, with an emphasis being given to equations arising in the construction of hermitian and Kahler metrics with prescribed curvature properties, for example, the complex Monge-Ampere equation in Kahler geometry. These equations are among the most important appearing in geometric analysis and understanding their structure and the techniques involved in their solution are paramount for further progress in the field. Recent breakthroughs include, among others, the independent construction of non-flat Ricci-flat Kahler metrics with maximal volume growth on complex affine space of dimensions three and greater by Li, Conlon-Rochon, and Szekelyhidi, the construction of new examples of gradient expanding and steady Kahler-Ricci solitons by Conlon-Deruelle and Biquard-Macbeth respectively, the solution by Chen-Cheng of a major conjecture of Tian stating the equivalence between the existence of constant scalar curvature Kahler metrics on a compact Kahler manifold and the properness of Mabuchi's K-energy, and the recent families of Ricci-flat Kahler metrics exhibited by Hein-Sun-Viaclovsky-Zhang on K3 surfaces which collapse to an interval with Tian-Yau and Taub-NUT metrics occurring as bubbles. These topics will all form part of the conversation at the conference in January 2021. More details will be available at http://faculty.fiu.edu/~rconlon/conference.htm\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": "10117", "attributes": { "award_id": "2048075", "title": "CAREER: Principled Deep Reinforcement Learning for Societal Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EPCN-Energy-Power-Ctrl-Netwrks" ], "program_reference_codes": [], "program_officials": [ { "id": 718, "first_name": "Donald", "last_name": "Wunsch", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-01", "end_date": "2026-01-31", "award_amount": 500000, "principal_investigator": { "id": 26022, "first_name": "Zhaoran", "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": 317, "ror": "https://ror.org/000e0be47", "name": "Northwestern University", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "The recent breakthrough in deep reinforcement learning (RL), especially its superhuman-level performance in board and video games, e.g., Go, Atari, Dota, and StarCraft, opens up new avenues for controlling many complex and unknown systems via learning. However, for practical purposes beyond game playing, deep RL still suffers from a lack of efficiency and trustworthiness. In terms of efficiency, the empirical success of deep RL requires millions to billions of data points and days to weeks of running time. In terms of trustworthiness, the empirical success of deep RL is only measured by the received reward, which does not account for safety and robustness. Such a lack of efficiency and trustworthiness is further exacerbated when we scale up deep RL to design and optimize societal systems in critical domains, e.g., healthcare, transportation, power grid, financial network, and supply chain.\n\nThis CAREER proposal addresses these challenges by establishing a theoretical framework for analyzing the computational efficiency and sample efficiency of single-agent deep RL and an algorithmic framework for achieving such efficiencies. Moreover, it leads to a stochastic game framework for achieving safety, robustness, scalability, fairness, risk-awareness, and incentivization in social systems via multi-agent deep RL. The research plan emphasizes connecting deep RL with multiple fields, e.g., nonconvex optimization, nonparametric statistics, causal inference, stochastic game, and social science. \n\nThe education plan emphasizes teaching data-driven decision making as a fundamental skill for future generations, especially for future leaders, in societal contexts. In particular, it aims to promote the idea of data-driven social leadership and support underrepresented minority researchers and students, who personally experience pressing challenges in societal systems, from K-12 education to graduate training. In order to cope with the ongoing pandemic, the outreach plan involves organizing online seminars on data science and artificial intelligence, mentoring remote interns by integrating research and education, and engaging remote students via DataFest and Client Project Challenge.\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": "10118", "attributes": { "award_id": "2105550", "title": "I-Corps: Learning-based Navigation Technology-enabled Service Robots for the Retail Industry", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-15", "end_date": "2022-07-31", "award_amount": 50000, "principal_investigator": { "id": 4408, "first_name": "Dinesh", "last_name": "Manocha", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Robotics']", "approved": true, "websites": "['http://gamma.umd.edu', 'https://gamma.umd.edu/researchdirections/covid19/c19', 'https://arxiv.org/abs/2008.06585']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the development of a retail service robot utilizing state-of-the-art, human-friendly navigation technology developed for densely crowded environments. As per the current industry estimates, retailers are losing $1 trillion worldwide due to inventory aberrations. Retail customers are frequently disappointed by out-of-stock items because of poor inventory management, leading to reduced customer loyalty. To survive current market trends in which most items may be purchased online, brick-and-mortar stores need to modernize, and use automated solutions wherever possible to free up associates’ time and focus more on the customer experience. \n\nThis I-Corps project is based on the development of a robot enabled with state-of-the-art collision avoidance technology. It is capable of operating in crowds of a density greater than 1 person/square meter. The robot detects pedestrian movements using commodity visual sensors and smartly plans a path that is least obtrusive to humans. The collision avoidance technology also significantly reduces (by > 50%) the occurrence of the robot suddenly freezing or halting, which is a common phenomenon in dense scenarios. The I-Corps program will help better understand the applications of such a robot in the retail industry. The proposed robot will navigate in a densely crowded store and will perform volumetric scans to capture real-time data about the items on the shelves and customer buying patterns, leading to an increase in efficiency and accuracy in managing a store’s inventory.\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": 1386, "pages": 1397, "count": 13961 } } }