Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1394&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1393&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=-keywords" }, "data": [ { "type": "Grant", "id": "10196", "attributes": { "award_id": "2118061", "title": "Collaborative Research: CyberTraining: Implementation: Medium: Cyber Training on Materials Genome Innovation for Computational Software (CyberMAGICS)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CyberTraining - Training-based" ], "program_reference_codes": [], "program_officials": [ { "id": 26118, "first_name": "Ashok", "last_name": "Srinivasan", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 705, "ror": "https://ror.org/002w4zy91", "name": "University of West Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] } ], "start_date": "2021-09-01", "end_date": "2025-08-31", "award_amount": 600000, "principal_investigator": { "id": 26142, "first_name": "Aiichiro", "last_name": "Nakano", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26140, "first_name": "Priya", "last_name": "Vashishta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26141, "first_name": "Ken-ichi", "last_name": "Nomura", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 152, "ror": "https://ror.org/03taz7m60", "name": "University of Southern California", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The computing landscape is evolving rapidly. Exascale computers can perform unprecedented mathematical operations per second, while quantum computers have surpassed the computing power of the fastest supercomputers. Concomitantly, artificial intelligence (AI) is transforming every aspect of science and engineering. To address these rapid changes and challenges, this project will train a new generation of materials cyberworkforce, who will solve challenging materials genome problems through innovative use of advanced cyberinfrastructure (CI) at the exa-quantum/AI nexus. Further, the project will foster the adoption of exa-quantum/AI nexus technologies by a broad research community and beyond through a unique dual-degree PhD/MS program, undergraduate research to close the research-education gap, and broadening participation of women and underrepresented groups.\n\nThis project will develop training modules for a new generation quantum materials simulator named AIQ-XMaS (AI and quantum-computing enabled exascale materials simulator), which integrates exa-scalable quantum, reactive and neural-network molecular dynamics simulations with unique AI and quantum-computing capabilities to study a wide range of materials and devices of high societal impact such as optoelectronics and pandemic preparedness. CyberMAGICS (cyber training on materials genome innovation for computational software) portal will be developed as a single-entry access point to all training modules that include step-by-step instructions in Jupyter notebooks and associated tutorial slides/videos, while providing online cloud service for those who do not have access to computing platform. The modules will be incorporated into the open-source AIQ-XMaS software suite as tutorial examples, and they will be piloted in classroom and workshop settings to directly train 1,200 CI users at the University of Southern California (USC) and Howard University, with a strong focus on underrepresented groups. Broader reach and training will be accomplished through the portal and nanoHUB. Students trained in the dual-degree program will earn a PhD in materials science or physics; they will also earn either an MS in computer science specialized in high-performance computing and simulations, MS in quantum information science, or MS in materials engineering with machine learning. Undergraduate students will be mentored and trained by academic scholars in multidisciplinary fields as well as by scientists at national labs and industry. The project will further broaden participation through USC’s Women in Science and Engineering (WiSE) program and undergraduate research by underrepresented groups jointly supervised by USC and Howard faculty.\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": "10197", "attributes": { "award_id": "2117282", "title": "MRI: Acquisition of a High-Performance Computer System to Support Research and Training in Computational Biology and Data Science at Meharry Medical College", "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": 1343, "first_name": "Marilyn", "last_name": "McClure", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2024-09-30", "award_amount": 671411, "principal_investigator": { "id": 26144, "first_name": "Aize", "last_name": "Cao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26143, "first_name": "Qingguo", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 938, "ror": "https://ror.org/00k63dq23", "name": "Meharry Medical College", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "This MRI aims to acquire a High-Performance Computing (HPC) System to support research and training in Computational Biology and Data Science in a Historically Black College and University (HBCU). The system will allow Meharry Medical College to: \n• Support and expand multidisciplinary research in big data and areas with high Computational needs; support Maharry’s planned school of Applied Computational Sciences by incorporating state-of-the-art \ngenetics studies, learning, data visualization, and other functions requiring high performance computing, and other functions, and other areas with high computational needs, \n• Assist in recruiting and retaining historically underrepresented minority students in computational sciences and biology, \n• Expand educational and research outreach in High Performance Computing in Middle Tennessee and other HBCU. \nThis HPC system aims at providing robust computational processing to support programs at the Data Science Institute. \n\nThis system will provide significant computational support for high throughput research and advance knowledge in computational biology and data science. This includes, but is not limited to big data management, sequencing data analyses, image processing, machine learning, and other types of data analyses (sequencing data and Acade analyses). The system offers a safe place for resource sharing, and contributes in retaining valuable knowledge, as well as providing opportunities for collaboration and engaging in trying to restrain the pandemic, and attaining more students.\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": "10198", "attributes": { "award_id": "2044502", "title": "I-Corps Hub: Mid-Atlantic Region", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps Hubs" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-01", "end_date": "2026-12-31", "award_amount": 15000000, "principal_investigator": { "id": 26146, "first_name": "Dean", "last_name": "Chang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26145, "first_name": "Darryll J", "last_name": "Pines", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 297, "ror": "https://ror.org/047s2c258", "name": "University of Maryland, College Park", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps Hubs project is to catalyze an inclusive Mid-Atlantic innovation network to spur the development of deep technology startups that will have much-needed economic and societal impacts regionally, nationally, and globally. Research universities are positioned to support commercialization of scientific discoveries, and NSF I-Corps accelerates the growth and impact of university-launched deep technology ventures. The uniquely-experienced Mid-Atlantic Region I-Corps Hub team will build on the initial success of I-Corps Nodes to continue the cultural transformation at several of the country's top research universities to embed innovative practices into the conceptualization and conduct of research; to solve critical societal problems; and to create jobs, opportunities, and economic value. This Hub project directly aligns with the goals of the American Innovation and Competitiveness Act, which is critically important for previously industrialized regions such as the Mid-Atlantic region as it is experiencing significant economic turmoil due to shifts in American manufacturing industries, compounded by disruptions due to the pandemic. Also, the Hub vision and strategy prioritizes diversity, equity, and inclusion, particularly among underrepresented groups and underserved minorities and minority institutions like Historically Black Colleges and Universities (HBCUs).\n\nThe I-Corps program curriculum is built on scientific methods that incorporate hypothesis testing, the build-measure-learn cycle, and continuous iteration. Multiple outcomes result from the program, teams: change research directions, get funding from grants or private investors, or form start-up companies and grow through revenue. Equally important, the impact of the program accrues over many years. The Hub model enables alignment of a systematic and comprehensive analysis of regional impacts, as well as the programmatic, environmental, and individual factors that mediate the efficacy of the program itself. Understanding for whom and under what conditions I-Corps programs are most effective, and what factors contribute to or detract from this effectiveness is critical to inform the continued growth and scaling of I-Corps.\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": "10199", "attributes": { "award_id": "2040381", "title": "ML Basis for Intelligence Augmentation:Toward Personalized Modeling, Reasoning under Data-Knowledge Symbiosis, and Interpretable Interaction for AI-assisted Human Decision-making", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Sci of Lrng & Augmented Intel" ], "program_reference_codes": [], "program_officials": [ { "id": 1414, "first_name": "Soo-Siang", "last_name": "Lim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2024-08-31", "award_amount": 738927, "principal_investigator": { "id": 26147, "first_name": "Eric", "last_name": "Xing", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 243, "ror": "", "name": "Carnegie-Mellon University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Much of the work people do today—in healthcare, business, scientific enterprises, and military operations—is performed in teams. Collaborative decision-making effort within a team is a complex and challenging process of integrating, understanding, and acting upon different types of information. This project aims to advance the use of artificial intelligence and machine learning as intelligence augmentation (IA) tools for facilitating and improving collaborative decision making in clinical teams, focusing on AI-assisted diagnosis and treatment. The focus of the investigators reflects the practical importance and impact of IA in healthcare, especially in the on-going fight with the pandemic where efficiency, validity, and cost-effectiveness of medical decision-making is critical. However, the proposed methods will apply to other forms and use-cases of IA, such as policy making, public health responses, intelligence and business operations, ultimately advancing national health, prosperity, and welfare.\n\nAlthough modern machine learning research has been widely involved in solving various pattern discovery and recognition tasks based on a wide spectrum of data—either in a fully autonomous fashion or in rudimentary human-AI collaborative settings such as crowdsourcing—effectively augmenting and assisting complex collaborative human decision-making efforts in the space of diagnosis, treatment, planning, logistics remains to be an open challenge. In clinical decision- making, understanding and treating the disease must rely on the vast knowledge and expertise and be based on evidence coming from heterogeneous sources of information, ranging from text (medical history), to imagery (radiograms), to time series data (vitals). Making sense of such multimodal information requires effective communication and collaboration within clinical teams. The investigators propose to study some of the key technical challenges in machine learning for IA: (1) modeling human decision-making processes; (2) incorporating background knowledge into data-driven systems; and (3) building human-AI interface for productive inter- and intra-team collaboration. To that end, the investigators will: (1) develop a machine learning framework based on modeling individual decision-makers that enables accurate detection of errors in medical diagnosis and can be used as a recommendation engine in collaborative decision-making settings; (2) develop principled strategies for integrating objective medical knowledge (e.g., automatically extracted from rapidly growing medical literature) with the clinical experience and expertise of a team of health professionals; (3) design human-interpretable interfaces that enable efficient communication in decision making within and across teams, including new tools for interpreting how the models arrived at each recommended decision and natural language interfaces that can facilitate human-AI collaboration.\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": "10200", "attributes": { "award_id": "2112878", "title": "HCC: Small: Mitigating Online Risks: Designing Social VR to Prevent New Forms of Online Harassment", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "HCC-Human-Centered Computing" ], "program_reference_codes": [], "program_officials": [ { "id": 849, "first_name": "Dan", "last_name": "Cosley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2024-09-30", "award_amount": 399785, "principal_investigator": { "id": 26148, "first_name": "Guo", "last_name": "Freeman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 290, "ror": "https://ror.org/037s24f05", "name": "Clemson University", "address": "", "city": "", "state": "SC", "zip": "", "country": "United States", "approved": true }, "abstract": "This project investigates new and potentially disruptive ways that people experience harassment in online social spaces, in order to design novel and safer social technologies to protect, promote trust, and mitigate emerging online risks. It focuses on newly emerging forms of online harassment in that are occurring in social Virtual Reality (VR), that is, novel and increasingly popular 3D virtual spaces, where multiple users interact with one another through VR head-mounted displays, synchronous voice conversation, avatars that track a person’s body movements, and simulated touching and grabbing features. As online social spaces evolve towards more natural and immersive interaction, social VR grows as an important and popular medium for social activities and connections in and beyond the era of the pandemic. In this way, the project directly promotes public interest in making VR research and innovation facilitate human activity. It contributes to the general public's social and emotional well-being by creating safer online social spaces and promoting healthier interaction dynamics in these spaces. \n\nThis project takes a proactive approach to examining and designing new VR systems for diverse users’ novel and safe social interactions. It is working toward active prevention of new and potentially more disruptive harassment in online environments and experiences through three phases: (1) explore how people experience and cope with harassment in social VR, using automatic online data scraping, participatory observations, and interviews; (2) theorize and model new forms of online harassment using a large-scale online survey; and (3) generate user-centered design patterns and guidelines to protect users from new forms of online harassment through prototyping, a participatory design process, and community deployment. Overall, the project is expected to advance knowledge about how to explicate risks and mitigate new forms of online harassment, which emerge in response to new technologies and social experiences.\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": "10201", "attributes": { "award_id": "2117564", "title": "Understanding the effect of individual decision-making strategies on collective decision outcomes", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "Decision, Risk & Mgmt Sci" ], "program_reference_codes": [], "program_officials": [ { "id": 577, "first_name": "Claudia", "last_name": "Gonzalez-Vallejo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2024-08-31", "award_amount": 476231, "principal_investigator": { "id": 26150, "first_name": "Chuqiao", "last_name": "Yang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 1358, "first_name": "Jeanine L", "last_name": "Skorinko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26149, "first_name": "Ani", "last_name": "Harutyunyan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 988, "ror": "https://ror.org/01arysc35", "name": "Santa Fe Institute", "address": "", "city": "", "state": "NM", "zip": "", "country": "United States", "approved": true }, "abstract": "Collective decisions are central to societies, from committees to governments. An important open question is how collective decisions are affected by individuals who follow the choices of others (social learners) instead of evaluating the merit of options on their own (individual learners). The three central questions guiding this project are: 1) Will a collective choose the best available option when many of its members are social learners? 2) How does the collective outcome depend on the properties of individuals and the options? and 3) How will social learners affect society’s ability to adjust to new evidence? This work expands the current understanding of collective decisions by examining a variety of factors simultaneously rather than in isolation. It helps anticipate and manage instabilities in important real-world collective decision-making systems such as democratic elections, jury decisions, and teamwork in companies. Moreover, the most pressing challenges facing humanity, such as climate change, technological disruption, and pandemics, are critically subject to global collective decisions. \n\nThe project investigates the effect of social learning on collective decision-making using a transdisciplinary combination of three methods. First, the program of research develops a dynamical-system model that studies how the interactions of social and non-social factors affect collective decisions and adjustments to new information. A preliminary model predicted a critical transition in opinion composition as the proportion of the social learners crossed a threshold. The current project expands this framework to include the presence of committed minorities, the introduction of new evidence about the merit of options, and the effect of network structures. The model derives conditions under which groups can settle on the option with the most merit. Second, the project tests the predictions of the model by collecting and analyzing observational and survey data and estimates the effect of social learning on beliefs and behaviors of real-world societies. Third, group-level and individual-level human participant experiments test model predictions in controlled conditions. The individual-level experiments uncover factors that influence individuals to adopt social or individual learning. This research informs a long-lasting debate on how individual learning strategies (individual or social) affect collective decisions. It combines and synthesizes social psychological principles and experiments, mathematical modeling methods, and statistical analysis. Results inform a wide range of audiences with the purpose of providing key insights for improving collective decisions in many real-world domains.\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": "10202", "attributes": { "award_id": "2118380", "title": "Bimodal Haptic-Mixed Reality (HMR) Needle Insertion Simulation for Hand-Eye Skills", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Cyberlearn & Future Learn Tech" ], "program_reference_codes": [], "program_officials": [ { "id": 1414, "first_name": "Soo-Siang", "last_name": "Lim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-15", "end_date": "2024-08-31", "award_amount": 850000, "principal_investigator": { "id": 26153, "first_name": "Kwangtaek", "last_name": "Kim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26151, "first_name": "Robert", "last_name": "Clements", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26152, "first_name": "Jeremy M", "last_name": "Jarzembak", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 547, "ror": "https://ror.org/049pfb863", "name": "Kent State University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "Although performing intravenous (IV) insertion is a very common medical procedure, it is technically difficult to master as demonstrated by the 35%-50% failure rate resulting in a negative cycle of re-insertions leading to increased patient harm and costs to the healthcare system. Faulty IV insertions in real-world conditions are related to vein variables (vein rolling or resistant to puncture) and patient variables (touch skin, skin coloring). Experts in nursing education have advocated for self-paced integration of simulation-based technologies to deliberately practice IV skills while receiving immediate feedback for error correction. However, using currently developed simulators or manikin arms fails to capture the actual realism and psychomotor techniques adaptive to variability needed to gain procedural mastery of the skill. To enhance the current learning environment, technological advances are needed to create a realistic learning platform with variability that maximizes the skill transfer and long-term retention. The proposed work is to fill the gap by developing a novel simulation system using haptics and mixed reality (HMR) and investigating the learning impacts. This work is significant because current haptic technologies combined with extended reality do not yet provide sufficient realism and variability to effectively develop the fine motor skills. Further, studies have not been conducted on the educational impact of bimodal HMR simulation with variable conditions that can adaptively create realistic patient environments during training. Upon developing the successful nature of the proposed research, new insight into effective learning technology as well as causes of improved learning in hand-eye skills will be provided, which may be used to improve learning in similar settings or be transformative to other fields such as cyber teaching and learning, hand skill training at work, immersive dexterous interfaces, motor skill development for people with disabilities, STEM learning, robotic surgery, and medical training.\n\nThis project will develop a bimodal HMR system, using emerging technologies, haptics and MR, to simulate IV needle insertion with variable conditions that will create a realistic learning environment for students to master insertion tactile skills using two hands; and investigate whether variability in practice (disuse theory) improves needle insertion skills. To achieve these goals, the project will be divided into two phases: Phase I and II. Phase I will focus on developing the bimodal haptic simulation using two complimentary haptic devices, a haptic glove and a stylus haptic device, integrated with MR to simulate virtual patients and IV needle insertion with variable training conditions (skin color and stiffness, vein rolled, or resistant to puncture). In Phase II studies, 360 (180 per year) nursing students will be randomly assigned to experience training sessions in one of the three modes (HMR-static, HMR-variable, manikin arm). To measure learners’ IV insertion skills, trained evaluators (faculty members) from the College of Nursing will observe and evaluate participants’ skills based on an established IV insertion skill checklist through exams. Post training surveys will be collected in terms of the realism and the user experience (usability) and those data will be used for continuously improving the HMR system. This research will advance the knowledge related to developing innovative learning and teaching environments using emerging technologies and provide empirical evidence of impactful variables that affect learning performance. The developed platform as an automatic self-practice system will provide free access to this medium for instructors and students alike in healthcare or related communities to extend and use even under a pandemic, for broadening participation for under-represented and financially challenged groups.\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": "10203", "attributes": { "award_id": "2120219", "title": "Collaborative Research: A Comprehensive Dynamic Search Framework for Asynchronous Multi-Objective Multi-Agent Planning", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "FRR-Foundationl Rsrch Robotics" ], "program_reference_codes": [], "program_officials": [ { "id": 818, "first_name": "Juan", "last_name": "Wachs", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-15", "end_date": "2024-08-31", "award_amount": 375930, "principal_investigator": { "id": 26154, "first_name": "Sivakumar", "last_name": "Rathinam", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 2151, "first_name": "Robin R", "last_name": "Murphy", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Robotics']", "approved": true, "websites": "['http://roboticsforinfectiousdiseases.org/index.html', 'http://roboticsforinfectiousdiseases.org/index.html']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 282, "ror": "", "name": "Texas A&M Engineering Experiment Station", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 282, "ror": "", "name": "Texas A&M Engineering Experiment Station", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Natural disasters are causing significant economic and human losses more than ever. The increase in frequency and intensity of these wide-scale disasters places additional strains on rescue workers in their heroic struggle to save lives and mitigate the impacts on health and the economy. As such, rescue workers must make critical decisions at high tempo. In these scenarios, information is key, and obtaining such information, as quickly as possible, saves lives. Therefore, this project develops search methods that can direct multiple heterogeneous agents to efficiently search and acquire information. This multi-agent planning is not just a problem in Humanitarian Assistance Disaster Relief (HADR) missions, but also in customized manufacturing, critical infrastructure management, pandemic response, automated construction of field hospitals, and post-disaster forensics. Since lives are on the line, the approaches used must not only find feasible paths that maximize the likelihood of finding life, but also finds “best” possible solutions within a given response time. Therefore, this project seeks to create a comprehensive framework to address a wide family of multi-agent multi-objective planning problems operating under several logistic constraints.\n\nThe intellectual merit of this project investigates how to couple, partially decouple, or completely decouple the coordinated planning of the agent's trajectories, and therefore defer planning until absolutely needed. As such, the work develops formal guarantees, either in terms of completeness and optimality properties, or approximation bounds, for the sub-optimal solutions obtained for various generalizations of the multi-agent problem. The performance of the approaches will be corroborated through large-scale simulations, and experiments on real robots. As part of the experimental process, this project will also define relevant metrics against which the new methods will be measured. This project will aim to substantiate that deferred planning, until necessary, offers computational, as well as path quality, and efficacy, benefits for several important generalizations of multi-agent path finding\nproblems.\n\nThis project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).\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": "10204", "attributes": { "award_id": "2133391", "title": "SCC-CIVIC-FA Track B: CaReDeX: Enabling Disaster Resilience in Aging Communities via a Secure Data Exchange", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "S&CC: Smart & Connected Commun" ], "program_reference_codes": [], "program_officials": [ { "id": 1663, "first_name": "David", "last_name": "Corman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2023-03-31", "award_amount": 999997, "principal_investigator": { "id": 881, "first_name": "Nalini", "last_name": "Venkatasubramanian", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 177, "ror": "", "name": "University of California-Irvine", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 877, "first_name": "Nikil D", "last_name": "Dutt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 879, "first_name": "Julie M", "last_name": "Rousseau", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 880, "first_name": "LIsa M", "last_name": "Gibbs", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26155, "first_name": "Sharad", "last_name": "Mehrotra", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 177, "ror": "", "name": "University of California-Irvine", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Disasters disproportionately impact older adults who experience increased fatality rates; such individuals often live in age-friendly communities and senior health facilities (SHFs). During a crisis, older adults are often unable to shelter safely in place or self-evacuate due to a range of physical conditions (need for life-sustaining equipment, impaired mobility) and cognitive afflictions (e.g. dementia, Alzheimer’s). First responders assisting older adults could benefit from seamless, real-time access to critical life-saving information about the living facilities (e.g., floor plans, operational status, number of residents) and about individual residents (e.g., health conditions such as need for dialysis, oxygen, personal objects to reduce anxiety). Such information, siloed within organizational logs or held by caregivers, is inaccessible and/or unavailable at the time of response. This interdisciplinary project brings together IT, geriatrics and resilience experts with disaster-response agencies and SHF providers to create information preparedness and transform disaster resilience for older adults.\n\nThe team will design, develop and deploy CareDEX, a novel community contributed data-exchange platform, that empowers SHFs to readily assimilate, ingest, store and exchange information, both apriori and in real-time, with response agencies to care for older adults in extreme events. The CareDEX information pipeline enables SHFs to capture individual information about changing health conditions and personalized needs and share them with responders to help improve response. Information co-produced with civic partners will identify and refine resident-specific data via tools for proactive collection/update. Given the sensitive nature of personal information, e.g., health-profiles, CareDEX will incorporate policy-based information sharing mechanisms that balance needs for individual privacy with authorized information release. CareDEX’s hybrid-cloud architecture seamlessly enables data to be securely stored on-premise (at SHF) and in the cloud for remote access by responders and temporary caregivers. Relocation of older adults requires regional information (e.g. road-conditions, facility status) - CareDEX will integrate GIS tools to provide first-responders with uptodate region-level situational awareness for dynamic decision-support. The prototype CareDEX platform will be co-developed with core civic partners, e.g. Front Porch (a nation-wide senior-care provider) and deployed at a SHF in Anaheim, CA. Collaborations with local response agencies (Los Angeles, Orange County, San Bernardino, San Diego) and national entities (FEMA, Red Cross, NFPA/FPRF) will mesh needs of emergency responders with caregivers. CareDEX will be evaluated using diverse scenarios - a wildfire event triggering relocation, wildfires coupled with a pandemic, and rapid onset earthquake events with small warning times and increased uncertainty.\n\nThe CIVIC Innovation Challenge is a collaboration with Department of Energy, Department of Homeland Security Science and Technology Directorate, Federal Emergency Management Agency (FEMA), and the National Science Foundation\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": "10205", "attributes": { "award_id": "2121410", "title": "RII Track-2 FEC: Genome Engineering to Sustain Crop Improvement (GETSCI)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office Of The Director", "EPSCoR Research Infrastructure" ], "program_reference_codes": [], "program_officials": [ { "id": 11391, "first_name": "J.D.", "last_name": "Swanson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2025-09-30", "award_amount": 3993756, "principal_investigator": { "id": 26160, "first_name": "Michael", "last_name": "Muszynski", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26156, "first_name": "Jianming", "last_name": "Yu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26157, "first_name": "Zhi-Yan", "last_name": "Du", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26158, "first_name": "Teresita D", "last_name": "Amore", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26159, "first_name": "Keunsub", "last_name": "Lee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 684, "ror": "", "name": "University of Hawaii", "address": "", "city": "", "state": "HI", "zip": "", "country": "United States", "approved": true }, "abstract": "Improved and practical crop breeding tools are required to meet the increasing demands of a growing global population and to overcome the sudden and variable stresses, made worse, by climate change. This project brings together researchers from the University of Hawai’i at Manoa and Iowa State University to develop an efficient, robust genome engineering toolkit that can be used to speed the generation of resilient crops adapted to a changing environment. Reproductive barriers are a major bottleneck that limits the genetic diversity available for crop improvement. Tropical maize germplasm is a rich source of genetic diversity but its flowering behavior in temperate regions precludes its broad use for maize improvement. To access this diversity, our two institutions formed a collaboration that integrates our strengths in tropical plant biology and transformation (Hawai’i) with maize transformation, genome engineering, and breeding (Iowa). Our goals are to establish a rapid and efficient genetic transformation platform and to develop improved genome editing tools to reprogram the flowering behavior of high-yielding tropical maize lines allowing their incorporation into any maize breeding program. Both Hawai’i and Iowa will gain a valuable new capability in genetic transformation and genome engineering which will transform the types of crop research possible at both institutions. Expected impacts from this project will help address food security and economic weaknesses in Hawai’i, by allowing for the development of new tropical crop breeding industries. In Iowa, access to gene-edited temperate-adapted tropical germplasm will move maize improvement into the next era of genome-optimized breeding. Workforce capacity will be increased by engaging underrepresented students, particularly Native Hawai’ians and Pacific Islanders, in diverse aspects of genome engineering research, by the exchange of undergraduates between partner institutions to prepare a globally competitive, multiculturally, and socially responsible workforce, and by creating opportunities for improved science communication skills through training sessions, workshops, and engagement with the community to communicate the value and safety of these new tools. \n\nCritical to our future is maintaining the rate of genetic improvement of the crops that feed us and sustain our economy. But the sudden and increasingly severe stresses caused by climate change limit the pace of improvement. Advances in genome engineering offer rapid solutions by enabling precise and targeted reprogramming of molecular networks to improve crop performance. The rich genetic diversity in tropical maize is largely underutilized for maize improvement because tropical lines are photoperiod sensitive and flower late in the long-days of temperate growing regions. To access this diversity, we formed a collaboration between the University of Hawai’i at Manoa (UH Manoa) and Iowa State University (ISU), which integrates strengths in tropical plant system biology and transformation (UH Manoa) with maize transformation, genome engineering, and breeding (ISU). Our goal is to use gene editing to suppress the photoperiod response in elite, high-yielding tropical maize to promote earlier flowering. These edited tropical lines can then be used to enhance any maize breeding program. Our objectives are to (1) establish an efficient, germplasm-independent maize transformation platform, (2) develop a facile, tractable genome editing toolkit to suppress the photoperiod response in six tropical inbreds, (3) analyze photoperiod network function in genome edited tropical lines, and (4) improve skills in communicating the value and safety of these new genome engineering tools. \nThe outcomes from this project include new tropical maize transformation capabilities at both jurisdictions, genome editing reagents for modulating flowering in maize, six elite tropical inbreds adapted to temperate breeding programs, a mechanistic understanding of the response to reprogramming the flowering network, and improved skills to communicate the value of this technology in professional and public contexts. Broader impacts expected from this project include opening this technology to academic labs, that can build research capacity by allowing genome engineering of diverse crops. Democratizing these tools are expected to speed breeding advancements, sustain crop improvement efforts, and spur economic growth. Both Hawai’i and Iowa will gain a valuable new capability in maize transformation and genome engineering, and will transform the types of crop research possible at both institutions. In Hawai’i, this project will help address food security and economic weaknesses revealed by the pandemic, by allowing for development of new tropical crop breeding industries. In Iowa, access to gene-edited temperate-adapted tropical germplasm moves maize improvement into the next era of genome-optimized breeding. Workforce capacity will be increased by engaging underrepresented students, particularly Native Hawai’ians and Pacific Islanders, in diverse aspects of genome engineering research, by the exchange of undergraduates between partner institutions to prepare a globally competitive, multiculturally, and socially responsible workforce, and by creating opportunities for improved science communication skills through training sessions, workshops, and engagement with the community to communicate the value and safety of these new tools.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1392, "pages": 1394, "count": 13934 } } }