Represents Grant table in the DB

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{
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    "data": [
        {
            "type": "Grant",
            "id": "3380",
            "attributes": {
                "award_id": "1828083",
                "title": "MRI: Acquisition of a Next Generation, Data-Centric Supercomputer",
                "funder": {
                    "id": 3,
                    "ror": "https://ror.org/021nxhr62",
                    "name": "National Science Foundation",
                    "approved": true
                },
                "funder_divisions": [
                    "Computer and Information Science and Engineering (CISE)",
                    "Major Research Instrumentation"
                ],
                "program_reference_codes": [],
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                    {
                        "id": 10745,
                        "first_name": "Alejandro",
                        "last_name": "Suarez",
                        "orcid": null,
                        "emails": "",
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                        "keywords": null,
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                ],
                "start_date": "2018-09-01",
                "end_date": "2021-08-31",
                "award_amount": 999000,
                "principal_investigator": {
                    "id": 10753,
                    "first_name": "Christopher",
                    "last_name": "Carothers",
                    "orcid": null,
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                        {
                            "id": 148,
                            "ror": "https://ror.org/01rtyzb94",
                            "name": "Rensselaer Polytechnic Institute",
                            "address": "",
                            "city": "",
                            "state": "NY",
                            "zip": "",
                            "country": "United States",
                            "approved": true
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                    {
                        "id": 10746,
                        "first_name": "Kristin",
                        "last_name": "Bennett",
                        "orcid": null,
                        "emails": "",
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                        "keywords": null,
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                    },
                    {
                        "id": 10748,
                        "first_name": "Mohammed J",
                        "last_name": "Zaki",
                        "orcid": null,
                        "emails": "",
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                        "keywords": null,
                        "approved": true,
                        "websites": null,
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                    },
                    {
                        "id": 10750,
                        "first_name": "George M",
                        "last_name": "Slota",
                        "orcid": null,
                        "emails": "",
                        "private_emails": "",
                        "keywords": null,
                        "approved": true,
                        "websites": null,
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                    },
                    {
                        "id": 10752,
                        "first_name": "Mark S",
                        "last_name": "Shephard",
                        "orcid": null,
                        "emails": "",
                        "private_emails": "",
                        "keywords": null,
                        "approved": true,
                        "websites": null,
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                    }
                ],
                "awardee_organization": {
                    "id": 148,
                    "ror": "https://ror.org/01rtyzb94",
                    "name": "Rensselaer Polytechnic Institute",
                    "address": "",
                    "city": "",
                    "state": "NY",
                    "zip": "",
                    "country": "United States",
                    "approved": true
                },
                "abstract": "The project will support the acquisition of a data centric supercomputer at Rensselaer Polytechnic Institute (RPI). This instrument will lead to significant advancements in science and engineering problems currently being tackled at RPI's Center for Computational Innovations (CCI) for applications including: the definition of new designed materials, applying active flows control for energy savings and microbiological systems modeling for medical treatment planning. The research will also include the development of new extreme-scale simulation technologies, graph analysis algorithms and the construction of entirely new simulation workflows. Hundreds of researchers and students from over 20 universities, 5 DOE national laboratories, 3 major industrial research centers (Corning, GE and IBM), 50 faculty, 4 start-ups across 11 U.S. states will take advantage of this proposed cyberinstrument to continue making a deep impact on their research. Student participation has been key to CCI's current success and national interest is anticipated not only due to the instrument's ability to advance current research but also due to its potential as a prototype model for future exascale systems.  Students engaged in projects supported by the instrument will become the next generation of compute and data intensive experts.\n\nThe new instrument integrates IBM POWER9 CPUs with next generation NVIDIA Volta GPUs into a hardware accelerated unified memory system (e.g., cache coherent). Additionally, all compute nodes are augmented with non-volatile memory storage, and a subset of the nodes include FPGA acceleration. The system will be used by faculty, students and CCI collaborators to address current barriers caused by the need to interact with massive data volumes that are used in and produced by next generation simulation tools. The cyberinstrument and algorithmic developments to be carried out will enable a new level of understanding and enhance our ability to solve many key challenges including: the accurate diagnosis of breast cancer directly from large-scale image datasets; semantic integration of the abundance of heterogeneous, multimodal, and multiscale data to improve personal health; modeling plasmas in fusion reactors; modeling active flow control devices that will greatly increase the weather conditions under which wind turbines will produce electricity; and combined biological data and model integration on molecular, cellular, and organ levels to understand organism-level phenomena and gain predictive understanding in systems biology.\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": "5400",
            "attributes": {
                "award_id": "0748453",
                "title": "Workshop on Frontiers in Electronics to be held on December 15-19, 2007 in Royal Park Hotel, Cozumel, Mexico",
                "funder": {
                    "id": 3,
                    "ror": "https://ror.org/021nxhr62",
                    "name": "National Science Foundation",
                    "approved": true
                },
                "funder_divisions": [
                    "Unknown",
                    "EPMD-ElectrnPhoton&MagnDevices"
                ],
                "program_reference_codes": [],
                "program_officials": [
                    {
                        "id": 18890,
                        "first_name": "Usha",
                        "last_name": "Varshney",
                        "orcid": null,
                        "emails": "",
                        "private_emails": "",
                        "keywords": null,
                        "approved": true,
                        "websites": null,
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                    }
                ],
                "start_date": "2007-09-01",
                "end_date": "2008-12-31",
                "award_amount": 10000,
                "principal_investigator": {
                    "id": 18891,
                    "first_name": "Michael",
                    "last_name": "Shur",
                    "orcid": null,
                    "emails": "",
                    "private_emails": "",
                    "keywords": null,
                    "approved": true,
                    "websites": null,
                    "desired_collaboration": null,
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                    "affiliations": [
                        {
                            "id": 148,
                            "ror": "https://ror.org/01rtyzb94",
                            "name": "Rensselaer Polytechnic Institute",
                            "address": "",
                            "city": "",
                            "state": "NY",
                            "zip": "",
                            "country": "United States",
                            "approved": true
                        }
                    ]
                },
                "other_investigators": [],
                "awardee_organization": {
                    "id": 148,
                    "ror": "https://ror.org/01rtyzb94",
                    "name": "Rensselaer Polytechnic Institute",
                    "address": "",
                    "city": "",
                    "state": "NY",
                    "zip": "",
                    "country": "United States",
                    "approved": true
                },
                "abstract": "Objective:\nThe objective of this proposal is to request funds from NSF to enable students and junior faculty to participate in the ?Workshop on Frontiers in Electronics? to be held 0n December 15-19, 2007 in Royal park Hotel, Cozumel, Mexico.  This conference is held every three years to review the rapid pace of electronic technology evolution that compels a merger of many technical areas. Examples of some of the areas that will be explored are bioelectronics, MEMS/NEMS, Silicon nanoelectronics and Beyond, High Speed Communications, etc.\n\nIntellectual Merit:\nThe main purpose of this workshop is to gather experts and to encourage cross fertilization of people with different technical backgrounds.  The experts will be gathered from academia, industry, and government to review the recent breakthroughs and their underlying physical mechanisms.  The workshop will also explore nature of the future challenges in this ?electronic Planet?.  The workshop also plans to prepare archival proceedings of peer reviewed article for broad distribution.  The proceedings will be published in the Special Issue of the ?International Journal of High Speed Electronics and Systems?. \n\nBroader Impact:\nWe plan to invite senior scientists and engineers as well as young faculty and students to this conference.  The conference will provide a Forum for open discussion, brain storming sessions and cross ?fertilization.  Some of the junior faculty will be invited to make presentations at the conference.  The outcome of the workshop, in the form of formal proceedings can be used as a teaching tool and for guiding future research areas",
                "keywords": [],
                "approved": true
            }
        },
        {
            "type": "Grant",
            "id": "8496",
            "attributes": {
                "award_id": "1R35GM141818-01",
                "title": "Hybrid Methods for Dynamic Structure Analysis of Proteins from Pathogenic Microorganisms",
                "funder": {
                    "id": 4,
                    "ror": "https://ror.org/01cwqze88",
                    "name": "National Institutes of Health",
                    "approved": true
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                "funder_divisions": [
                    "National Institute of General Medical Sciences (NIGMS)"
                ],
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                    {
                        "id": 23658,
                        "first_name": "FRANK PAUL",
                        "last_name": "Shewmaker",
                        "orcid": null,
                        "emails": "",
                        "private_emails": "",
                        "keywords": null,
                        "approved": true,
                        "websites": null,
                        "desired_collaboration": null,
                        "comments": null,
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                    }
                ],
                "start_date": "2021-07-01",
                "end_date": "2026-04-30",
                "award_amount": 658000,
                "principal_investigator": {
                    "id": 24259,
                    "first_name": "GAETANO T",
                    "last_name": "MONTELIONE",
                    "orcid": null,
                    "emails": "",
                    "private_emails": "",
                    "keywords": null,
                    "approved": true,
                    "websites": null,
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                        {
                            "id": 148,
                            "ror": "https://ror.org/01rtyzb94",
                            "name": "Rensselaer Polytechnic Institute",
                            "address": "",
                            "city": "",
                            "state": "NY",
                            "zip": "",
                            "country": "United States",
                            "approved": true
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                },
                "other_investigators": [],
                "awardee_organization": {
                    "id": 148,
                    "ror": "https://ror.org/01rtyzb94",
                    "name": "Rensselaer Polytechnic Institute",
                    "address": "",
                    "city": "",
                    "state": "NY",
                    "zip": "",
                    "country": "United States",
                    "approved": true
                },
                "abstract": "This research program will investigate the general hypothesis that understanding the conformational diversity of proteins will provide new insights into their biology, and enable medical research. It is directed to two classes of systems: Integral Membrane Proteins (IMPs) and viral-host interactions. IMPs play critical roles as gate keepers, receptors, transporters, homeostasis regulators, and drug targets. These functions are mediated by the conformational plasticity of the IMP in the membrane environment. IMPs are challenging to prepare, and even more challenging to reconstitute in appropriate membrane mimicking environments. Cost-effective technologies for isotope-enrichment in condensed volumes, hybrid approaches combining NMR with evolutionary co-variation (ECs), novel methods of contact prediction, and innovative modeling methods from the protein structure prediction community, will be applied to structure-function studies of IMPs. These IMPs, chosen from important human pathogens, including E. coli, K. pneumoniae, and P. aeruginosa, are potential targets for antibiotic discovery. ECs will also be combined with NMR data to determine structures of multiple “native states” of proteins. The second component of our program is directed to viral – host biomolecular complexes, and antiviral drug discovery. We will utilize innovative paramagnetic NMR methods, together with small angle X-ray scattering (SAXS), electron-electron double resonance spectroscopy (DEER), and Förster resonance energy transfer (FRET), to rigorously define dynamic interdomain structural distributions conferred by the partially-ordered linkers of the murine Moloney Leukemia Virus (MLV) integrase (IN). These data will be interpreted in the context of maximum occupancy probabilities (MaxOcc), and used to probe the role(s) of this flexibility in the gene integration mechanisms of g-retroviruses. Interdomain linkers also function to provide flexibility needed for binding partner promiscuity. We will also determine how the interdomain linker sequences of influenza Non-Structural Protein 1 (NS1) confer appropriate plasticity to define its specificity and affinity for host proteins and RNAs. This structural and functional promiscuity underlies NS1’s mechanisms for suppressing the cellular innate immune response to influenza infection, and rigorous characterization of its dynamic structural basis will provide fundamental information for live-attenuated virus vaccine development. We will also apply our platform to investigate drugs that inhibit SARS-CoV2 virus by binding its main protease (Mpro). We have identified three drugs, already approved for use in humans, originally designed to inhibit the NSP3/4A protease of hepatitis C virus, that also inhibit SARS-CoV2 in viral replication assays at low micromolar concentrations. Our computational docking studies have also identified several other FDA- approved drugs that may inhibit Mpro. Enzyme kinetic, biophysical chemistry, and X-ray crystallography studies will be used to characterize complexes formed between these protease inhibitor drugs and Mpro, and to develop their potential as COVID-19 therapeutics, or as lead compounds for new therapeutic development.",
                "keywords": [
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                    "Antibiotics",
                    "Antiviral Agents",
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                    "vaccine development"
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        },
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            "type": "Grant",
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            "attributes": {
                "award_id": "5R35GM141818-02",
                "title": "Hybrid Methods for Dynamic Structure Analysis of Proteins from Pathogenic Microorganisms",
                "funder": {
                    "id": 4,
                    "ror": "https://ror.org/01cwqze88",
                    "name": "National Institutes of Health",
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                        "id": 23658,
                        "first_name": "FRANK PAUL",
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                "start_date": "2021-07-01",
                "end_date": "2026-04-30",
                "award_amount": 658000,
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                    "id": 24259,
                    "first_name": "GAETANO T",
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                            "city": "",
                            "state": "NY",
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                "abstract": "This research program will investigate the general hypothesis that understanding the conformational diversity of proteins will provide new insights into their biology, and enable medical research. It is directed to two classes of systems: Integral Membrane Proteins (IMPs) and viral-host interactions. IMPs play critical roles as gate keepers, receptors, transporters, homeostasis regulators, and drug targets. These functions are mediated by the conformational plasticity of the IMP in the membrane environment. IMPs are challenging to prepare, and even more challenging to reconstitute in appropriate membrane mimicking environments. Cost-effective technologies for isotope-enrichment in condensed volumes, hybrid approaches combining NMR with evolutionary co-variation (ECs), novel methods of contact prediction, and innovative modeling methods from the protein structure prediction community, will be applied to structure-function studies of IMPs. These IMPs, chosen from important human pathogens, including E. coli, K. pneumoniae, and P. aeruginosa, are potential targets for antibiotic discovery. ECs will also be combined with NMR data to determine structures of multiple “native states” of proteins. The second component of our program is directed to viral – host biomolecular complexes, and antiviral drug discovery. We will utilize innovative paramagnetic NMR methods, together with small angle X-ray scattering (SAXS), electron-electron double resonance spectroscopy (DEER), and Förster resonance energy transfer (FRET), to rigorously define dynamic interdomain structural distributions conferred by the partially-ordered linkers of the murine Moloney Leukemia Virus (MLV) integrase (IN). These data will be interpreted in the context of maximum occupancy probabilities (MaxOcc), and used to probe the role(s) of this flexibility in the gene integration mechanisms of g-retroviruses. Interdomain linkers also function to provide flexibility needed for binding partner promiscuity. We will also determine how the interdomain linker sequences of influenza Non-Structural Protein 1 (NS1) confer appropriate plasticity to define its specificity and affinity for host proteins and RNAs. This structural and functional promiscuity underlies NS1’s mechanisms for suppressing the cellular innate immune response to influenza infection, and rigorous characterization of its dynamic structural basis will provide fundamental information for live-attenuated virus vaccine development. We will also apply our platform to investigate drugs that inhibit SARS-CoV2 virus by binding its main protease (Mpro). We have identified three drugs, already approved for use in humans, originally designed to inhibit the NSP3/4A protease of hepatitis C virus, that also inhibit SARS-CoV2 in viral replication assays at low micromolar concentrations. Our computational docking studies have also identified several other FDA- approved drugs that may inhibit Mpro. Enzyme kinetic, biophysical chemistry, and X-ray crystallography studies will be used to characterize complexes formed between these protease inhibitor drugs and Mpro, and to develop their potential as COVID-19 therapeutics, or as lead compounds for new therapeutic development.",
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        },
        {
            "type": "Grant",
            "id": "9620",
            "attributes": {
                "award_id": "2214216",
                "title": "Collaborative Research: HNDS-R: Dynamics and Mechanisms of Information Spread via Social Media",
                "funder": {
                    "id": 3,
                    "ror": "https://ror.org/021nxhr62",
                    "name": "National Science Foundation",
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                "funder_divisions": [
                    "Social, Behavioral, and Economic Sciences (SBE)",
                    "Human Networks & Data Sci Infr"
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                        "id": 2764,
                        "first_name": "Patricia Van",
                        "last_name": "Zandt",
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                ],
                "start_date": "2022-08-15",
                "end_date": "2025-07-31",
                "award_amount": 336644,
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                    "id": 25419,
                    "first_name": "Boleslaw",
                    "last_name": "Szymanski",
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                    "approved": true
                },
                "abstract": "There has never been so much information available at everyone’s fingertips than there is today. Unfortunately, with so much information  comes a lot of misinformation that can be spread to human populations and adopted by them as the truth. Understanding how information flows and its impact on human behavior is important for determining how to protect society from the effects of misinformation, propaganda, and “fake news.”   This project traces how information spreads on social media channels and how ideas, opinions, and beliefs change as they spread.  Conducting this research requires combining concepts from computational social sciences, computer science, sociology, and statistics to understand the fundamentals of information spread in social media\n\nThis project develops a new approach to the study of information diffusion that brings together several different mechanisms for information flow.  Together these are used to analyze how information spreads in social media.  The research has two main goals: First, it will spot and predict opinion trends and identify users’ polarization on topics of broad interest to society (e.g., climate change or the Covid-19 pandemic). Second, it will track information propagation to understand its role in shaping opinion trends and identify the factors that are important for its spread and adoption. The researchers have access to a large amount of data that permits them to build and test large-scale models of information diffusion.  The outcomes of this project include new computer algorithms that are capable of understanding information flow in social media and new avenues for research in the science of information spread and diffusion.\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": "11028",
            "attributes": {
                "award_id": "2303000",
                "title": "Collaborative Research: NSF-CSIRO: Fair Sequential Collective Decision-Making",
                "funder": {
                    "id": 3,
                    "ror": "https://ror.org/021nxhr62",
                    "name": "National Science Foundation",
                    "approved": true
                },
                "funder_divisions": [
                    "Computer and Information Science and Engineering (CISE)",
                    "Robust Intelligence"
                ],
                "program_reference_codes": [],
                "program_officials": [
                    {
                        "id": 2534,
                        "first_name": "Roger",
                        "last_name": "Mailler",
                        "orcid": null,
                        "emails": "",
                        "private_emails": "",
                        "keywords": null,
                        "approved": true,
                        "websites": null,
                        "desired_collaboration": null,
                        "comments": null,
                        "affiliations": []
                    }
                ],
                "start_date": "2023-09-01",
                "end_date": "2026-08-31",
                "award_amount": 299977,
                "principal_investigator": {
                    "id": 2616,
                    "first_name": "Lirong",
                    "last_name": "Xia",
                    "orcid": null,
                    "emails": "[email protected]",
                    "private_emails": "",
                    "keywords": null,
                    "approved": true,
                    "websites": null,
                    "desired_collaboration": null,
                    "comments": null,
                    "affiliations": [
                        {
                            "id": 148,
                            "ror": "https://ror.org/01rtyzb94",
                            "name": "Rensselaer Polytechnic Institute",
                            "address": "",
                            "city": "",
                            "state": "NY",
                            "zip": "",
                            "country": "United States",
                            "approved": true
                        }
                    ]
                },
                "other_investigators": [],
                "awardee_organization": {
                    "id": 148,
                    "ror": "https://ror.org/01rtyzb94",
                    "name": "Rensselaer Polytechnic Institute",
                    "address": "",
                    "city": "",
                    "state": "NY",
                    "zip": "",
                    "country": "United States",
                    "approved": true
                },
                "abstract": "This project aims to develop (artificial intelligence) AI-powered approaches to address challenging societal problems, such as dealing with droughts, infectious diseases, and environmentally harmful emissions. This project engages specific questions in these areas, such as: How can one effectively allocate water resources to increase agricultural drought resilience during drought seasons? How can one effectively determine when and where to construct hydrogen or electric vehicle refueling stations to encourage citizens to adopt these technologies to lower emissions? How can one effectively distribute vaccines and other medical supplies daily to enhance response to infectious diseases during pandemics? These problems belong to a class of classical and important problems in sequential collective decision-making. While sequential decision-making and collective decision-making have been studied previously, decision-making problems that are simultaneously sequential and collective are poorly understood, especially for specific domains such as resource allocation and when combined with goals such as responsibility and equitability. The overarching project goal is to establish theoretical and algorithmic foundations for responsible and equitable AI-powered sequential, collective decision-making. It also seeks to ensure that sequences of decisions satisfy multiple objectives and make appropriate trade-offs between short and long-term rewards subject to fairness criteria. The proposed research will lead to efficient and fair solutions to our social good and use-inspired applications in drought resilience, towards net zero, and infectious disease resilience. \n\nThe project will achieve its goals by focusing on three interconnected challenges, leveraging a wide range of techniques from AI, economics, and operation research. First, the challenge of fair multi-objective collective decision-making for a single time period subject to multiple objectives and fairness criteria. Second, explore fair sequential multi-objective collective decision-making that addresses trade-offs between immediate and long-term efficiency and fairness. Finally, understand the strategic aspects of fair multi-objective collective decision-making in collaboration with stakeholders, who provide information that facilitates the process.\n\nThis is a joint project between United States and Australian researchers funded by the Collaboration Opportunities in Responsible and Equitable AI under the U.S. NSF and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO).\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": "12792",
            "attributes": {
                "award_id": "2406647",
                "title": "CRII:III:Towards Advanced Filtering and Pooling Operations for Graph Neural Networks",
                "funder": {
                    "id": 3,
                    "ror": "https://ror.org/021nxhr62",
                    "name": "National Science Foundation",
                    "approved": true
                },
                "funder_divisions": [
                    "Computer and Information Science and Engineering (CISE)",
                    "Info Integration & Informatics"
                ],
                "program_reference_codes": [],
                "program_officials": [
                    {
                        "id": 946,
                        "first_name": "Hector",
                        "last_name": "Munoz-Avila",
                        "orcid": null,
                        "emails": "",
                        "private_emails": "",
                        "keywords": null,
                        "approved": true,
                        "websites": null,
                        "desired_collaboration": null,
                        "comments": null,
                        "affiliations": []
                    }
                ],
                "start_date": "2023-10-15",
                "end_date": null,
                "award_amount": 175000,
                "principal_investigator": {
                    "id": 28706,
                    "first_name": "Yao",
                    "last_name": "Ma",
                    "orcid": null,
                    "emails": "",
                    "private_emails": "",
                    "keywords": null,
                    "approved": true,
                    "websites": null,
                    "desired_collaboration": null,
                    "comments": null,
                    "affiliations": []
                },
                "other_investigators": [],
                "awardee_organization": {
                    "id": 148,
                    "ror": "https://ror.org/01rtyzb94",
                    "name": "Rensselaer Polytechnic Institute",
                    "address": "",
                    "city": "",
                    "state": "NY",
                    "zip": "",
                    "country": "United States",
                    "approved": true
                },
                "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>In recent years, we have witnessed a rapid growth in our ability to generate and gather data from numerous platforms in the online world and various sensors in the physical world. Graphs provide a universal representation for a variety of data including online social networks, knowledge graphs, transportation networks, and chemical compounds. Entities can usually be represented as nodes while their relations can be denoted represented as edges. Many important real-world applications on these data can be treated as computational tasks on graphs. A crucial step to facilitate these tasks is to learn good vector representations either for nodes or graphs. Recently, graph neural networks, which generalize deep learning techniques to graphs, have been widely adopted to learning representations for graphs. Though graph neural networks have advanced numerous real-world applications from various fields, they still suffer from many limitations in terms of efficacy and efficiency.  This project aims to address these limitations by conducting theoretical analysis and developing innovative algorithms. This project is specifically motivated by applications to computational social science, computational biology, and fraud detection in e-commerce. Furthermore, this project will involve graduate and undergraduate students in pursuing their theses or honor projects. Discoveries and research findings of this project will be tightly integrated into several current and new courses at the New Jersey Institute of Technology.<br/><br/>The technical aims of the project are divided into two tasks corresponding to the two major building components of graph neural networks: graph filtering operations and graph pooling operations. The graph filtering operation aims to refine node representations for all nodes in a graph. On the other hand, the graph pooling operation aims to summarize node representations to obtain a graph representation. The first task aims to investigate graph filtering operations under heterophily—a setting typically poses great challenges for graph filtering operations. In particular, the investigator will conduct theoretical analyses on graph filtering operations to gain deeper insights into their intrinsic mechanism, especially under the scenario of heterophily. Then, based on these understandings, more advanced graph neural networks models will be proposed to handle heterophilous graphs. The second task aims to develop more efficient and effective graph pooling operations. The investigators will explore and develop graph pooling operations based on clustering and down-sampling process. To improve the efficacy and efficiency of the graph pooling operations, the clustering/down-sampling process will be nicely incorporated into the entire learning framework in an end-to-end way.<br/><br/>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": "12469",
            "attributes": {
                "award_id": "2319438",
                "title": "URoL:ASC: What rules of life allow collectives to effectively manage risk? Understanding the rules underlying risk management across systems to increase societal resilience",
                "funder": {
                    "id": 3,
                    "ror": "https://ror.org/021nxhr62",
                    "name": "National Science Foundation",
                    "approved": true
                },
                "funder_divisions": [
                    "Biological Sciences (BIO)",
                    "URoL-Understanding the Rules o"
                ],
                "program_reference_codes": [],
                "program_officials": [],
                "start_date": "2023-09-01",
                "end_date": null,
                "award_amount": 0,
                "principal_investigator": {
                    "id": 28408,
                    "first_name": "Theodore",
                    "last_name": "Pavlic",
                    "orcid": null,
                    "emails": "",
                    "private_emails": "",
                    "keywords": null,
                    "approved": true,
                    "websites": null,
                    "desired_collaboration": null,
                    "comments": null,
                    "affiliations": []
                },
                "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": "Societies’ capacities to effectively manage risk, such as the threats arising from natural disasters, have not kept up with the world’s ecological changes.  Previously very rare events, such as large floods or long-lasting droughts, are becoming more frequent, and the rapid dissemination of information on the internet is contributing to the spread of misinformation about hazards, risks, and how to manage them.  To better deal with these risks, this project builds new risk management strategies that are based on biological Rules of Life.  These rules are used by living systems to preserve and protect the life in those systems, including those based on altruism, community growth, communication, and enforcement of community rules.  Biological systems that exploit these rules include bacterial colonies, hives of social insects, schools of fish, and herding animals.This project combines gamification of the Rules of Life with narrative storytelling to develop new strategies for collectively managing risk of natural disasters, infrastructure challenges, pandemics, and other shocks. The researchers use a practice-based co-design process that conducts science with involvement of individuals in at-risk communities.   Story- and play-based activities that require solving cooperation and coordination dilemmas create a variety of experiences and products that uncover new solutions to societal challenges, encourage cooperation and collective risk management, determine new ways to encourage people to engage collective risk management strategies, and develop new outreach activities, such as museum exhibits and workshops.  The project will benefit vulnerable low-income communities struggling to deal with disasters and water managers in the desert southwest trying to increase the resilience of the water supply.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": "12586",
            "attributes": {
                "award_id": "2301335",
                "title": "Collaborative Research: CISE-MSI: DP: CNS: An Edge-Based Approach to Robust Multi-Robot Systems in Dynamic Environments",
                "funder": {
                    "id": 3,
                    "ror": "https://ror.org/021nxhr62",
                    "name": "National Science Foundation",
                    "approved": true
                },
                "funder_divisions": [
                    "Computer and Information Science and Engineering (CISE)",
                    "CISE MSI Research Expansion"
                ],
                "program_reference_codes": [],
                "program_officials": [],
                "start_date": "2022-10-01",
                "end_date": null,
                "award_amount": 0,
                "principal_investigator": {
                    "id": 28510,
                    "first_name": "Pooyan",
                    "last_name": "Fazli",
                    "orcid": null,
                    "emails": "",
                    "private_emails": "",
                    "keywords": null,
                    "approved": true,
                    "websites": null,
                    "desired_collaboration": null,
                    "comments": null,
                    "affiliations": []
                },
                "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": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Multi-robot systems consist of autonomous robots interacting in a shared environment to achieve common goals. They are widely used in real-world application domains such as transportation, disaster management, as well as warehousing and manufacturing. This project develops an efficient, robust, and secure multi-robot system, called EdgeRobot. EdgeRobot establishes an edge computing based architecture and algorithmic framework to facilitate multi-robot collaboration and coordination in dynamic environments. This work provides new model, architecture, and theory for coordinated multi-robot systems. In addition, this project builds research capacity, sustainable for training underrepresented students via the partnership of six geographically diverse minority-serving institutions in the United States: the University of Houston-Clear Lake (South), the University of Michigan Flint (North), CUNY-New York City College of Technology (Northeast), Morgan State University (East), San Francisco State University (West), and California State University Dominguez Hills (West). The cross-institutional collaboration not only boosts research capacity in all six participating institutions but also provides integrative research and education experience to their underrepresented minority students. Ultimately, this project establishes and exemplifies an effective collaboration model for training and educating underrepresented students from geographically diverse minority-serving institutions.This project consists of the following three research thrusts. First, the novel edge computing infrastructure provides optimal and location-aware computing services for collaborative robots to achieve their common goals. Besides, reinforcement learning-based algorithms solve the multi-robot scheduling and routing problems, modeled as variants of the prize-collecting traveling salesman problem. Second, in tasks requiring collaborative actions, such as cooperative target tracking, multi-agent reinforcement learning enables teams of robots to operate, learn, and adapt in dynamic and human-populated environments robustly and safely. Third, integrating modern cryptographic and security primitives secures the collaboration among edge nodes in multi-robot systems. Consequently, the interface between EdgeRobot and its human team members builds a shared autonomy model.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": "12653",
            "attributes": {
                "award_id": "2325166",
                "title": "Collaborative Research: Understanding the hydrologic consequences of urban irrigation across the U.S.",
                "funder": {
                    "id": 3,
                    "ror": "https://ror.org/021nxhr62",
                    "name": "National Science Foundation",
                    "approved": true
                },
                "funder_divisions": [
                    "Geosciences (GEO)",
                    "Hydrologic Sciences"
                ],
                "program_reference_codes": [],
                "program_officials": [],
                "start_date": "2022-10-01",
                "end_date": null,
                "award_amount": 0,
                "principal_investigator": {
                    "id": 8115,
                    "first_name": "Diane",
                    "last_name": "Pataki",
                    "orcid": null,
                    "emails": "",
                    "private_emails": "",
                    "keywords": null,
                    "approved": true,
                    "websites": null,
                    "desired_collaboration": null,
                    "comments": null,
                    "affiliations": [
                        {
                            "id": 202,
                            "ror": "https://ror.org/03r0ha626",
                            "name": "University of Utah",
                            "address": "",
                            "city": "",
                            "state": "UT",
                            "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": "All components of the water cycle are altered by human activities in cities, and the impacts of these changes on urban water and climate are still poorly understood. Urbanization affects climate, the amount of water in soil (soil moisture), and the type and amount of vegetation across the landscape. All of these factors strongly impact evapotranspiration (ET): the flux of water from land to the atmosphere. Urban ET is poorly predicted by hydrologic models that do not adequately represent human actions, such as irrigation. Yet, urban irrigation can have large effects on climate, soil moisture, and plant growth and survival. This study addresses the extent to which ET is limited by soil moisture, atmospheric water demand (a function of humidity and air temperature), or the density and distribution of vegetation within and across U.S. cities. Measurements will be made in three urban regions: Los Angeles, CA (a semi-arid city where irrigation has been declining due to drought response policy), Salt Lake City, UT (semi-arid but still heavily irrigated), and Tallahassee, FL (high rainfall and very high urban tree cover). These cities represent urban settings with different water cycle components. This project will advance knowledge and understanding of urban ET, improve basic climate and water cycle models, and to contribute to efficient water management in cities and urban landscapes.   Urban hydrologic data are still sparse relative to observations in natural and agricultural systems. To advance a generalizable understanding of urban hydrology, it is necessary to explore categorizable differences within and across cities in the balance of water supply (soil moisture as supplied by both irrigation and precipitation), plant demand for water uptake (as determined by the magnitude, distribution, and composition of leaf area), and atmospheric evaporative demand (net radiation and vapor pressure deficit). The project will examine similarities and differences in these fluxes within and across cities by quantifying irrigation efficiency, its variability, and its key drivers. The contribution of each component of the soil-plant-atmosphere system to ET fluxes is likely to vary in mesic vs. arid/semi-arid climates and according to local irrigation practices as well as urbanization processes that influence land and vegetative cover. By sampling cities that are hypothesized to span different combinations and ranges of irrigation practices and likely limits on urban soil moisture, vapor pressure deficit, and ET, the investigators will test a general framework that can be applied beyond these specific cities and measurement sites. Ultimately, this project will use the extensive datasets collected in this study for advancing mechanistic models of urban landscape ET as an alternative to empirical crop and landscape coefficient approaches. The results will be disseminated to stakeholders and extension specialists who are focused on improving turfgrass management, outdoor water management, and urban water policy. The investigators will also leverage programs for recruiting and retaining undergraduate and graduate students from under-represented groups to build a diverse, interdisciplinary team aimed at broadening participation in STEM.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
            }
        }
    ],
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