Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=-awardee_organization
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-awardee_organization", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-awardee_organization", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-awardee_organization", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1390&sort=-awardee_organization" }, "data": [ { "type": "Grant", "id": "1673", "attributes": { "award_id": "2041666", "title": "EAGER: Joint Hazard Mitigation in the Era of COVID-19: Implications for Engineered Structures and Services", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "036E", "039E", "040E", "043E", "096Z", "1057", "1576", "7916", "CVIS" ], "program_officials": [ { "id": 4382, "first_name": "Joy", "last_name": "Pauschke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2022-08-31", "award_amount": 299824, "principal_investigator": { "id": 4386, "first_name": "David", "last_name": "Mendonca", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Industrial engineering']", "approved": true, "websites": "['https://homepages.rpi.edu/~mendod/']", "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": [ { "id": 4383, "first_name": "Tracy L", "last_name": "Kijewski-Correa", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4384, "first_name": "Julio A", "last_name": "Ramirez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 4385, "first_name": "Ann-Margaret", "last_name": "Esnard", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 148, "ror": "https://ror.org/01rtyzb94", "name": "Rensselaer Polytechnic Institute", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Much emphasis during the response to the ongoing COVID-19 pandemic has rightly been on traditional public health efforts at controlling it; however, less prominent but no less vital is the role of the built environment itself in both amplifying and suppressing the effects of COVID-19. In the former case, this includes densely-packed, highly centralized physical work spaces, while in the latter this includes adaptive use of decentralized physical work spaces (such as private homes) or virtual ones (as for online learning). The prospect of co-occurrence of natural hazards (such as hurricanes, tornadoes and earthquakes) during the COVID-19 regime is likely to strain and possibly confound ongoing and future response efforts. Accordingly, this EArly-concept Grant for Exploratory Research (EAGER) will explore the role of engineered structures and services within the built environment in order to improve efforts to prevent pandemic joint hazards from becoming societal disasters. This will require basic research in exploring new theories, methods, data and technologies for supporting mitigation, together with collaborations with multiple organizations, including the NSF-supported Natural Hazards Engineering Research Infrastructure and its components (https://www.DesignSafe-ci.org). This project will contribute to NSF's role in the National Earthquake Hazards Reduction Program (NEHRP) and the National Windstorm Impact Reduction Program (NWIRP). This project will develop and disseminate a research framework and corresponding research agenda to support improved understanding of the role of the built environment in mitigating or amplifying risks associated with pandemic joint hazards. Case study data will be collected and analyzed, leading to an initial research framework. The research agenda will be developed with close cooperation from a broad and diverse set of researchers and practitioners in the hazards domain, resulting in a set of fundamental methodological, empirical and conceptual challenges around this topic. The project’s potentially radical re-examination of contemporary notions of hazard mitigation and performance-based engineering, as well as its engagement of new interdisciplinary approaches to understanding the relationship between physical and virtual facilities/services in mitigating pandemic joint hazards, represents a high-risk endeavor falling well outside the intellectual boundaries of current civil infrastructure and natural hazards research. The results of this work are expected to spur new lines of inquiry in various branches of engineering, potentially informing advances well beyond this project. Ultimately, this project's holistic and contextualized approach will contribute to the design of a more equitable, functional and safer built environment, well suited to a future that is likely to be marked by highly disruptive pandemics occurring jointly with other hazards.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": "2744", "attributes": { "award_id": "1849607", "title": "Doctoral Dissertation: Genetic Screening at Home: Interpretive Policy Analysis of Direct-to-Consumer Genetic Testing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "STS-Sci, Tech & Society" ], "program_reference_codes": [], "program_officials": [ { "id": 8110, "first_name": "Wenda K.", "last_name": "Bauchspies", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-04-01", "end_date": "2021-08-31", "award_amount": 16020, "principal_investigator": { "id": 8112, "first_name": "Abby", "last_name": "Kinchy", "orcid": null, "emails": "", "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": [ { "id": 8111, "first_name": "Hined A", "last_name": "Rafeh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "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 examines federal regulations regarding direct-to-consumer advertising of genetic testing. It asks two main questions: (1) How are US federal regulatory agencies like the Food and Drug administration categorizing, analyzing, and regulating direct-to-consumer genetic testing; and (2) What influence do other groups such as professional organizations, advocacy groups, and research institutes have on the regulation of direct-to-consumer genetics? While scholars have critically examined how genetic tests perpetuate certain identities and social categories, we know little about the processes by which genetic testing is being regulated. The present study advances our understanding of this issue in relation to three major trends occurring in biomedicine. These include a shift from genetic diagnosis to genetic risk, the redefinition of the patient as a consumer, and the de-medicalization of genetics. This project thus increases our scholarly understanding of direct-to-consumer genetic testing as it is unfolding within broader social and cultural transformations occurring in medicine and health. \n\nThis is a multi-method social science investigation of factors shaping federal regulations regarding direct-to-consumer genetic testing. Primary data for the project come from archival documents, interviews, and participant observations. The main goal of the project is to understand how federal agencies conceptualize and regulate direct-to-consumer advertising of genetic tests, and the relative power of outside stakeholder groups to shape federal regulations surrounding commercial genetic testing. Undertaking this research enables an enhanced understanding of ethical issues surrounding direct-to-consumer genetic testing and of how the concepts of risk, diagnoses, and genetics are used in these contexts and in the context of current transformations in medicine and health. Results will be shared with the broader community to educate citizens about the regulation of direct-to-consumer genetic testing and about the influence of stakeholders in the federal regulatory process.\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": "2861", "attributes": { "award_id": "1922633", "title": "PFI-TT: Next Generation Lithium-Metal Batteries for High Performance, Low Cost and Safe Energy Storage", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "PFI-Partnrships for Innovation" ], "program_reference_codes": [], "program_officials": [ { "id": 8559, "first_name": "Kaitlin", "last_name": "Bratlie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-07-01", "end_date": "2021-09-30", "award_amount": 245500, "principal_investigator": { "id": 8561, "first_name": "Nikhil", "last_name": "Koratkar", "orcid": null, "emails": "", "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": [ { "id": 8560, "first_name": "Rahul", "last_name": "Mukherjee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "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 broader impact/commercial potential of this Partnerships for Innovation (PFI-TT) project is to enable the development of high performance, low cost and safe Lithium-metal batteries. This is expected to translate into breakthrough improvements in energy density for next generation energy storage and could deliver ~2X higher performance and ~3X lower cost than the current best Lithium-ion batteries. This would enable significant reduction in battery weight, size and cost for laptops, cell phones, wearable devices, all-electric vehicles, as well as grid storage. Beyond performance and cost, a major focus of this project is on battery safety. In particular, we will explore novel methods to prevent short circuiting and heating problems in lithium-metal batteries, thereby rendering them safe. The deliverable for this project will be a minimum viable product that can be subjected to third-party validation and also field-tested by potential customers. Such testing will provide a detailed understanding of device level performance, cost and safety metrics and will be instrumental in advancing this technology towards commercial viability. Our traineeship model aims to develop a robust entrepreneurial and innovation ecosystem. For this, a broad range of activities (such as entrepreneurship training, communication skills development, venture support, internships etc.), will be offered to help students develop a holistic view of their research. \n\nThe proposed project aims to replace graphitic anodes with a vastly superior alternative in Lithium-ion batteries. Instead of storing lithium atoms within a graphite host, we propose to store Lithium in its metallic form, which leads to much higher energy density. However, the Achilles heel for any Lithium-metal battery is the uncontrolled growth of dendrites which can electrically short the battery. We propose to overcome this challenge by utilizing a novel approach that involves applying high current densities of optimized magnitude (for healing purposes) to generate internal self-heat in the battery. This controlled heating triggers massive surface diffusion of Lithium on the dendritic surface, which blunts the dendrite tips and smoothens the Lithium metal surface. Our main objective is to demonstrate safe (i.e., dendrite-free) operation of high energy density and low cost Lithium-metal batteries in larger format pouch and cylindrical cells and perform detailed device level characterization.\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": "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": [], "program_officials": [ { "id": 10745, "first_name": "Alejandro", "last_name": "Suarez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "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, "emails": "", "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": [ { "id": 10746, "first_name": "Kristin", "last_name": "Bennett", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 10748, "first_name": "Mohammed J", "last_name": "Zaki", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 10750, "first_name": "George M", "last_name": "Slota", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 10752, "first_name": "Mark S", "last_name": "Shephard", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "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, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "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, "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": "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 }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "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, "affiliations": [] } ], "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, "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 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": [ "2019-nCoV", "Affinity", "Antibiotics", "Antiviral Agents", "Attenuated Live Virus Vaccine", "Binding", "Biological Assay", "Biology", "COVID-19 therapeutics", "Communities", "Complex", "Coronavirus", "Data", "Docking", "Drug Targeting", "Electrons", "Environment", "Enzyme Kinetics", "Escherichia coli", "FDA approved", "Fluorescence Resonance Energy Transfer", "Gammaretrovirus", "Genes", "Genome", "Hepatitis C virus", "Homeostasis", "Human", "Hybrids", "Influenza", "Innate Immune Response", "Integral Membrane Protein", "Integrase", "Isotopes", "Klebsiella pneumoniae", "Lead", "Mediating", "Medical Research", "Membrane", "Methods", "Modeling", "Molecular Conformation", "Moloney Leukemia Virus", "Nonstructural Protein", "Pathogenicity", "Peptide Hydrolases", "Pharmaceutical Preparations", "Play", "Probability", "Protease Inhibitor", "Protein Analysis", "Proteins", "Pseudomonas aeruginosa", "RNA", "Reproducibility", "Research Project Summaries", "Roentgen Rays", "Role", "SH2D3C gene", "Specificity", "Spectrum Analysis", "Structure", "System", "Technology", "United States National Institutes of Health", "Variant", "Viral", "Virus", "Virus Replication", "X-Ray Crystallography", "biophysical chemistry", "cost effective", "design", "drug discovery", "flexibility", "human pathogen", "influenza infection", "influenzavirus", "innovation", "insight", "microorganism", "novel", "novel therapeutics", "pathogenic bacteria", "priority pathogen", "programs", "protein structure prediction", "receptor", "reconstitution", "therapeutic development", "vaccine development" ], "approved": true } }, { "type": "Grant", "id": "8497", "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", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "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, "affiliations": [] } ], "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, "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 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": [ "Affinity", "Antibiotics", "Antiviral Agents", "Attenuated Live Virus Vaccine", "Binding", "Biological Assay", "Biology", "COVID-19 therapeutics", "Communities", "Complex", "Coronavirus", "Data", "Docking", "Drug Targeting", "Electrons", "Environment", "Enzyme Kinetics", "Escherichia coli", "FDA approved", "Fluorescence Resonance Energy Transfer", "Gammaretrovirus", "Genes", "Genome", "Hepatitis C virus", "Homeostasis", "Human", "Hybrids", "Influenza", "Innate Immune Response", "Integral Membrane Protein", "Integrase", "Isotopes", "Klebsiella pneumoniae", "Lead", "Mediating", "Medical Research", "Membrane", "Methods", "Modeling", "Molecular Conformation", "Moloney Leukemia Virus", "Nonstructural Protein", "Pathogenicity", "Peptide Hydrolases", "Pharmaceutical Preparations", "Play", "Probability", "Protease Inhibitor", "Protein Analysis", "Proteins", "Pseudomonas aeruginosa", "RNA", "Reproducibility", "Research Project Summaries", "Roentgen Rays", "Role", "SARS-CoV-2 inhibitor", "SH2D3C gene", "Specificity", "Spectrum Analysis", "Structure", "System", "Technology", "United States National Institutes of Health", "Variant", "Viral", "Virus", "Virus Replication", "X-Ray Crystallography", "biophysical chemistry", "cost effective", "design", "drug discovery", "flexibility", "human pathogen", "influenza infection", "influenzavirus", "inhibitor therapy", "innovation", "insight", "microorganism", "novel", "novel therapeutics", "pathogenic bacteria", "priority pathogen", "programs", "protein structure prediction", "receptor", "reconstitution", "therapeutic development", "vaccine development" ], "approved": true } }, { "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", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Human Networks & Data Sci Infr" ], "program_reference_codes": [], "program_officials": [ { "id": 2764, "first_name": "Patricia Van", "last_name": "Zandt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-15", "end_date": "2025-07-31", "award_amount": 336644, "principal_investigator": { "id": 25419, "first_name": "Boleslaw", "last_name": "Szymanski", "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": "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 } } ], "meta": { "pagination": { "page": 1391, "pages": 1405, "count": 14046 } } }