Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1383&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=1394&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1382&sort=-keywords" }, "data": [ { "type": "Grant", "id": "10106", "attributes": { "award_id": "2212297", "title": "Collaborative Research: CNS Core: Medium: Innovating Volumetric Video Streaming with Motion Forecasting, Intelligent Upsampling, and QoE Modeling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CSR-Computer Systems Research" ], "program_reference_codes": [], "program_officials": [ { "id": 3557, "first_name": "Erik", "last_name": "Brunvand", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2026-09-30", "award_amount": 400000, "principal_investigator": { "id": 4281, "first_name": "Lili", "last_name": "Qiu", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Volumetric video is an emerging type of multimedia content. Unlike traditional videos and 360-degree videos that are two-dimensional (2D), every frame in a volumetric video consists of a three-dimensional (3D) scene or object represented by a 3D model such as a point cloud or a polygon mesh. The 3D nature of volumetric video enables viewers to move with six degrees of freedom, leading to a truly immersive viewing experience. However, compared to conventional videos, streaming volumetric videos over the Internet faces unique challenges: a lack of precise motion tracking, extremely high bandwidth consumption, a lack of quality-of-experience (QoE) models, and complex interactions among network, computation, and motion. This project proposes a holistic research agenda addressing the above challenges through four core approaches: multi-dimension, multi-modality motion sensing and prediction; deep-learning-based content upsampling; comprehensive QoE modeling; and resource/motion adaptation. The project aims at demonstrable systems and networking research with a synergy among multimedia systems, networking, wireless sensing, machine learning, computer vision, and graphics. The research team will develop prototypes and evaluate them in real-world settings. \n\nInternet video streaming is playing a key role in today's world, especially during the COVID-19 pandemic. As a key enabler of immersive telepresence, volumetric content will become popular in the near future. The techniques developed from this project will significantly reduce network resource usage and improve the user experience for volumetric content delivery over the Internet. This will benefit both producers and consumers of volumetric videos, as well as boost their applications in various domains such as education, telehealth, manufacturing, and entertainment, ultimately leading to a high impact on societies and economies. The project will also offer new education components, and provide a platform for various Broadening Participation in Computing (BPC) activities.\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": "10107", "attributes": { "award_id": "2035041", "title": "EAGER: Open access resources to promote undergraduate neuroscience education", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Organization" ], "program_reference_codes": [], "program_officials": [ { "id": 7758, "first_name": "Evan", "last_name": "Balaban", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2023-12-31", "award_amount": 299164, "principal_investigator": { "id": 26013, "first_name": "Elizabeth", "last_name": "Kirby", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 308, "ror": "", "name": "Ohio State University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "Every year, hundreds of thousands of undergraduate students participate in an Introduction to Behavioral Neuroscience course. This gateway course, offered by Neuroscience, Psychology and Biology departments across the country, provides students with foundational principles of nervous system structure and function, as well as basic information about how the nervous system governs movements, feelings, and thought. Given the high enrollments in introductory behavioral neuroscience courses across the US, the development of a high-quality, open access textbook could substantially reduce barriers to neuroscience education, as well as implement a broad core curriculum that may broadly improve educational quality. The recent shift of numerous courses to online format in the wake of COVID19 further highlights the need for digital learning resources that can be accessed from anywhere in the world. This project will create an open access, online educational resource to serve as a replacement for commercial textbooks. It will integrate multimedia presentation of material, including videos and high quality graphics, as well as instructor-friendly supplemental material (including sample test questions and pre-prepared lecture slides), providing access to a faculty-created, peer-reviewed neuroscience educational resource that is not sequestered behind a pay-wall. Wider access to professional neuroscience learning resources is likely to translate into improved recruitment of talented students into neuroscience. Although targeted toward US students, the common adoption of US textbooks in international classrooms means that this resource would also be poised to influence international neuroscience education and recruitment.\n\nTextbooks support and guide learning in numerous college-level science courses. Though faculty typically create the content of science textbooks, the books are primarily produced and sold by for-profit publishers. Instructors then assign texts which students pay to access, by purchasing a physical book or gaining online access. The emergence of open educational resources offers a way to reduce financial barriers to academic success by providing educational materials free of charge, to anyone who wishes to learn from them. However, there are currently no high-quality open access textbook-replacement resources for university undergraduate neuroscience students. To address this gap, the proposed work will develop an open-access, online textbook replacement to accompany the typical Introduction to Behavioral Neuroscience course for first and second year college students. The proposed content will be created, curated and peer-reviewed by a team of expert neuroscientists who all show excellence in undergraduate instruction. Resource production, formatting and access will be supported by a team experienced in the dissemination of open access textbooks (OpenStax). The proposed resource will be rooted in digital presentation, allowing written text to be interleaved with high quality graphics and embedded videos. Slides with images and videos will also be provided for instructors to encourage faculty adoption of the resource, as will a test bank. Most importantly, all of these components will be free and openly accessible to the public.\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": "10108", "attributes": { "award_id": "2114529", "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": "2022-01-31", "award_amount": 9999, "principal_investigator": { "id": 5378, "first_name": "Lydia", "last_name": "Bourouiba", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 210, "ror": "https://ror.org/042nb2s44", "name": "Massachusetts Institute of Technology", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 210, "ror": "https://ror.org/042nb2s44", "name": "Massachusetts Institute of Technology", "address": "", "city": "", "state": "MA", "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": "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 } } ], "meta": { "pagination": { "page": 1383, "pages": 1394, "count": 13934 } } }