Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1383&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=1424&sort=-awardee_organization", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-awardee_organization", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1382&sort=-awardee_organization" }, "data": [ { "type": "Grant", "id": "972", "attributes": { "award_id": "2105953", "title": "Collaborative Research: Research Initiation: Social Engagement & Belonging in Academic Makerspaces", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 2357, "first_name": "Jumoke", "last_name": "Ladeji-Osias", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-06-15", "end_date": "2023-05-31", "award_amount": 60914, "principal_investigator": { "id": 2358, "first_name": "Audrey R", "last_name": "Boklage", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Academic makerspaces provide students with open access to resources that help them develop their problem solving skills, provide opportunities for collaboration, and encourage experimentation and discovery. However, recent research has shown that many makerspace environments do not readily support diverse populations, create tensions between different student groups, and can sometimes lead to a work environment that feels exclusive and unwelcoming. It is essential that our academic makerspaces are welcoming and that all students feel a sense of belonging and acceptance in these spaces. This project will use the flexible nature of an academic makerspace as the framework to provide students with the opportunity to connect socially in ways that have been shown to increase sense of belonging. This will be accomplished by integrating social engagement activities into a university affiliated makerspace. The engagement activities will be designed to support students’ social and emotional development, which is an essential component to creating culturally competent, well-rounded engineers. Due to the flexible and informal nature of the makerspace environment, it is an ideal place to build and create social connections between students. Makerspaces provide a venue for informal learning and student connection that inspires attributes associated with the professional formation of engineers: creativity, discovery, lifelong learning, teamwork, and critical thinking. This study will lay the foundation for future research that will inform strategies to create an equitable and inclusive makerspace culture with a focus on better supporting non-dominate students.This project aims to increase student sense of belonging in undergraduate engineering students through the integration of social engagement activities into an academic makerspace. The objectives of this project are to (1) strategically integrate social engagement activities that have been shown to contribute to increased sense of belonging and student persistence into an academic makerspace; (2) research the effects of the social engagement activities on student sense of belonging; and (3) increase engineering education research capacity at Western Washington University. The outcomes of this work will lead to identification of best practices for improving student sense of belonging in a makerspace environment. This research will investigate the impact of carefully designed social engagement activities on development of student sense of belonging. Within the recent context of the COVID-19 pandemic, social support and positive sense of belonging have been shown to counter the adverse social emotional effects of the experience. Engagement activities will focus on supporting student social and emotional development, providing peer support, and building awareness of the importance of equity, inclusion, and diversity in engineering. The activities will be designed so that any student can participate regardless of ability level, time availability, or physical location. The research questions that will guide this work are (1) To what extent do students participate in the engagement activities within the makerspace and in which formats, and does this level of engagement vary based on student demographics? and (2) To what extent does participation in the engagement activities lead to an increased sense of belonging? These research questions will be investigated through a communities of practice theoretical framework using a two-year mixed methods research that includes survey development, analysis, student reflections, and focus groups.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": "1043", "attributes": { "award_id": "2107524", "title": "Collaborative Research: HCC: Medium: Fine-grained Emotion Analysis in Crises", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 2566, "first_name": "Todd", "last_name": "Leen", "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": 452312, "principal_investigator": { "id": 2568, "first_name": "Junyi", "last_name": "Li", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 2567, "first_name": "James W", "last_name": "Pennebaker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "History is rich with situations where the same event has been interpreted completely differently by different groups of people. Through events such as the OJ Simpson case, the COVID-19 crisis, and the murder of George Floyd, we have observed disparate reactions to events that community leaders, police departments, policymakers, and everyday citizens fail to anticipate. The purpose of this project is to begin to identify social, emotional, and linguistic markers of crises (e.g., social turmoil, natural disasters, etc.) that predict the various ways people will react to the same events. This is achieved by analyzing the language of social media, a rapidly-growing source of data from which we can understand the expression and perception of emotions at a very large scale, with far-reaching potential uses from academic research to public policy.Understanding emotions, the context surrounding these emotions, and subsequent behaviors are of great value to those in a crisis, seeking information about a crisis, or helping manage responses to a crisis. This project will discover mechanisms to provide comprehensive, fine-grained emotion analysis across different social platforms, and derive robust and reliable predictive models. Fine-grained emotion analysis aims to: (1) detect expressions of emotions in a text and characterize their intensity and polarity, (2) identify the triggers causing the emotions, and (3) analyze emotion deviation (i.e., the varied emotions that people express towards the same trigger). This research will contribute annotated datasets of emotions expressed on social media across distinct crises and generalizable models equipped with deep linguistic understanding for contextualized emotion analysis. Industry and academic partners will participate by evaluating the ability of the models to work on situations and data sources different from those used to develop the models.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": "1084", "attributes": { "award_id": "2133106", "title": "ASCENT: Multimodal chest e-tattoo with customized IC and deep learning algorithm for tracking and predicting progressive pneumonia", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 2695, "first_name": "Zhengda", "last_name": "Zhengdao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-15", "end_date": "2025-08-31", "award_amount": 1500000, "principal_investigator": { "id": 2700, "first_name": "Nanshu", "last_name": "Lu", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 2696, "first_name": "Hongyu", "last_name": "Miao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 480, "ror": "https://ror.org/03gds6c39", "name": "The University of Texas Health Science Center at Houston", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, { "id": 2697, "first_name": "Craig", "last_name": "Rusin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2698, "first_name": "Shaolan", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2699, "first_name": "Parag N", "last_name": "Jain", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Coronavirus infections may cause life-threatening pneumonia with a mortality rate more than 10% in certain populations, which could quickly overwhelm any medical care system. Continuous monitoring of the infected and suspected at the hospital or under self-quarantine can help optimize triage and treatment. However, so far there is no available mobile device and algorithm platform that can perform reliable, comprehensive, continuous and long-term monitoring and assessment for pneumonia patients in either clinical or free-living environments. The goal of this ASCENT research is to develop, integrate, and test foundational technologies required for a scalable monitoring and triage system for patients who have contracted pneumonia. The objective is to integrate a wireless, noninvasive, week-long wearable, and multimodal physiological sensor platform (e-tattoos) with a dedicated integrated circuit (IC), connect it to an FDA (U.S. Food and Drug Administration) cleared virtual patient monitoring platform (Sickbay) which also hosts a customized deep learning algorithm, for the continuous monitoring and assessment of the severity of progressive pneumonia. The result will be a gamechanging hardware and software system that provides continuous monitoring and intelligent assessment for highly-infectious and critically-ill patients but also protects healthcare providers from infection and contamination.There is a longstanding systems challenge that the world lacks long-term, high-fidelity, continuous and scalable clinical surveillance platforms for infectious disease patients to battle with global pandemic like COVID-19. The progression of pneumonia is associated with the changes in vital signs such as core body temperature, respiratory rates, heart rates, blood oxygen saturation and so on. Since clinical deterioration of patients at risk of developing pneumonia can be short and unpredictable, continuous multimodal monitoring and accurate assessment is necessary for this population, whether in the hospitals or at home. The five investigators bring together well-established expertise in multimodal wearable sensors (Lu), mixed signal IC design (Li), time-series data analytics (Miao), clinical systems integration and scalable patient monitoring (Rusin), as well as critical care medicine (Jain). This multidisciplinary engineering and clinical team attempt to address this system-level challenge through: 1) development of wireless wearable sensors called e-tattoo with dedicated IC capable of noninvasive and week-long multimodal patient monitoring; 2) data analysis and deep learning algorithm development and integration with e-tattoo through an FDA (U.S. Food and Drug Administration) cleared virtual patient monitoring platform, Sickbay; 3) e-tattoo and algorithm validation on 20 patients with progressive pneumonia at Texas Children’s Hospital. The broader impacts for the society are dramatically improving how critically ill patients are monitored as well as training next generation engineers to carry out convergent research. The ultimate vision is to establish a scalable means of safely surveilling patients and orchestrating high-quality care across the country.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": "1227", "attributes": { "award_id": "2032434", "title": "RAPID/Collaborative Research: Implications of Social Distancing Policies on Water Infrastructure Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "029E", "036E", "096Z", "7914", "9102" ], "program_officials": [ { "id": 3148, "first_name": "Yueyue", "last_name": "Fan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2022-04-30", "award_amount": 169591, "principal_investigator": { "id": 3152, "first_name": "Kasey M", "last_name": "Faust", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3149, "first_name": "Kerry A", "last_name": "Kinney", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3150, "first_name": "Lynn E", "last_name": "Katz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3151, "first_name": "Polina", "last_name": "Sela", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This Rapid Response Research (RAPID) grant will demonstrate how drinking water services provided by water utilities have been impacted by the social distancing policies undertaken by much of the nation during the 2020 coronavirus pandemic. By design, social distancing policies have caused immense change in human behavior, as families shelter in place and industrial and commercial sectors pause operations. One of the many consequences of these changes is a significant spatial, temporal, and volumetric change in water use within municipal water distribution systems. These changes have the potential to substantially impact water age and water quality both within the distribution system and within homes and other buildings. In addition, utilities are facing unprecedented workforce, financial, and logistical challenges because of the pandemic as they respond to the unknown and emerging consequences of social distancing policies. As such, this research has the potential to prevent infrastructure asset damage and negative public health impacts in communities with piped water that have undertaken social distancing. Accordingly, and, as warranted by results, this project will create technical guidance for utilities to proactively mitigate these potential negative consequences.In this research, existing water meter data and new interview data will be used to identify changes in water use patterns associated with various social distancing regimes. Next, the impacts of these changes on the pressure and water quality in distribution systems will be evaluated via hydraulic modeling and water quality sampling and analysis. The hydraulic modeling will identify and locate potential water distribution problems in a university campus, a residential neighborhood, and a primarily commercial, urban downtown. These data, coupled with physical, chemical, and microbiological water quality analyses, will provide vital new empirical knowledge of water infrastructure performance under social distancing conditions. Interview data from utilities around the nation will further validate findings and enable the generalization of results and potential mitigation strategies. In partnership with collaborating water utilities, these combined datasets will be integrated to create a range of possible future Impact Scenarios of social distancing for water distribution systems. The empirically validated hydraulic models will be used to determine the implications of each Impact Scenario on distribution system performance, and to test mitigation strategies that utilities can implement during anticipated future waves of the pandemic and social distancing. Broadly, this research builds engineering theory to link human patterns of water use to the performance and resilience of water distribution infrastructure.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": "1289", "attributes": { "award_id": "2029692", "title": "RAPID/Collaborative Research: Human-AI Teaming for Big Data Analytics to Enhance Response to the COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "041E", "042E", "096Z", "7914", "9102" ], "program_officials": [ { "id": 3312, "first_name": "Daan", "last_name": "Liang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2021-04-30", "award_amount": 20262, "principal_investigator": { "id": 3313, "first_name": "Keri K", "last_name": "Stephens", "orcid": "https://orcid.org/0000-0002-9526-2331", "emails": "[email protected]", "private_emails": "", "keywords": "['Communication']", "approved": true, "websites": "['https://orgcommtech.org/', 'https://orgcommtech.org']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Social media data can provide important clues and local knowledge that can help emergency managers and responders better comprehend and capture the evolving nature of many disasters. Yet humans alone cannot grasp the vast data generated by social media, so computers are used to assist. Very little is currently known about how to leverage the skills of humans and machines when they work together (human-machine teaming) to identify meaningful patterns in social media data. Therefore, the fundamental issues this Rapid Response Research (RAPID) project seeks to address are 1) understanding the process of real-time decisions that human digital volunteers make when they rapidly convert social media data into structured codes the machine (Artificial Intelligence algorithms) can understand, and 2) using this knowledge to improve human-machine teaming. This project advances the field by revealing the unique abilities that both humans and machines bring when working together to comprehend social media patterns during an evolving disaster. It supports education and diversity by providing research experiences to diverse students, as well as generating data useful for interdisciplinary courses teaching teamwork, social media analysis, and human-machine teaming. Finally, the findings can help emergency managers better train their volunteers who comb through social media using their understanding of the local knowledge and built environment to help machines see new patterns in data. Hence, this project supports NSF's mission to promote the progress of science and to advance the nation's health, prosperity, and welfare by articulating the unique value that both humans and computers bring that can lead to better decisions during disasters. The goal of this research is to better understand the real-time decisions that human annotators make under different environmental constraints, and how those contribute to the learning of Artificial Intelligence (AI) models. Under time constraints and information overload, human decision-making capabilities are limited; yet, humans still have a unique ability to understand the contextual references to the structures in the built environment that machines cannot recognize. For example, the meaning of the tweet, “Memorial is overloaded,” -- which means the hospital, called Memorial, is out of beds for patients —- can be lost on AI systems that lack the knowledge of the built environment. This example demonstrates the value that humans in the loop offer in a human-AI teaming context. This research focuses on capturing the ephemeral data from a variety of social media sources and our two research thrusts include: 1) online observations of Community Emergency Response Team (CERT) volunteers and a manager (a collaborator on this project) using think-aloud and cognitive interviewing strategies to reveal the real-time mental models used to make coding decisions for annotation tasks; and 2) an empirical analysis of different sampling algorithms for active (machine) learning paradigms to develop a typology of machine errors under diverse contexts that affect the quality of human decision making for annotation. This research will generate design guidelines that bridge the gap between the mechanisms used for real-time data processing with AI models and the understanding of context contributed by a human user teaming with the AI models. Using theories of human decision-making combined with knowledge of how AI functions, this project provides a real-time, mid-disaster examination of 1) how humans understand, process, and interpret social media messages, and 2) how to refine AI algorithms to optimize active learning paradigm. This understanding will provide a theoretical framework enabling future research to develop protocols to optimize human-AI teaming by using concepts such as motivation and information theory. This work can help emergency managers conduct better training of their CERT volunteers and other annotators and provide clearer guidelines for how to communicate the unique value that humans bring to the annotation process for AI systems. Both our protocols and developed understanding of how humans interact with AI systems will be helpful for global health organizations, local and state-level disaster decision-makers, as well as provide direction for the vast CERT network in the United States.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": "1345", "attributes": { "award_id": "2028521", "title": "RAPID: Collaborative Research: Transforming passive protective face masks toward active capture and inactivation of coronavirus with nano-assisted surfactant modification", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7237", "7914" ], "program_officials": [ { "id": 3464, "first_name": "Nora", "last_name": "Savage", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2022-04-30", "award_amount": 92402, "principal_investigator": { "id": 3465, "first_name": "Navid", "last_name": "Saleh", "orcid": "https://orcid.org/0000-0001-6092-5783", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['https://www.caee.utexas.edu/prof/saleh/index.html']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Novel coronavirus disease is caused by SARS-CoV-2, an extremely virulent strain of coronavirus that is responsible for an increasing number of illnesses and deaths, globally. According to the World Health Organization, “SARS-CoV-2 is transmitted via droplets and fomites during close unprotected contact between an infector and an infectee”. A recent study has shown that these virions can be airborne for at least 30 minutes and be active on the aerosol droplets up to three hours. Non-pharmaceutical protection is essential to stunt the spread of this virus. As of March 19, 2020, the Center for Disease Control’s official guidelines include non-optimal reuse of surgical masks for extended periods of time. Then, on March 27, 2020, the World Health Organization declared a critical emergency on personal protection equipment. The reuse of masks puts healthcare workers, first line responders, patients, and the community at risk. The aim of this research project between the University of Texas-Austin and the University of Florida Health Shands Hospital is to develop and implement a rapid protocol for extended use of surgical and N95 masks. The researchers will develop a fast, simple procedure for turning passive masks into active protective gear.This proposal addresses a critical need to improve personal protection equipment during the coronavirus crisis. A new extended use protocol for masks will render immediate benefits to the healthcare community. An enhanced mechanistic understanding of virion capture and inactivation at nano-bio interfaces will have wide applicability in the design of personal protective equipment. This research project has the following objectives: (i) obtain masks from the Shands hospital, treat them with surfactants, ultraviolet-C ray, and steam, and then characterize the efficacy of each treatment using viability tests, (ii) modify mask surfaces with surfactant-modified (sodium lauryl sulfate, butadines, or stearoyl lactylate) carbon particles (activated carbon, carbon quantum dots, or nano-diamond) at different ratios and moisture content and then test their efficacy in attaching and inactivating coronaviruses, (iii) understand the mechanisms of virus attachment and inactivation, and (iv) implement a rapid extended mask-use program. The outcomes from this project should have critical and widespread scientific and public health impacts. The modified masks could provide needed support to the depleted mask-inventory of health care providers.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": "1382", "attributes": { "award_id": "2027426", "title": "RAPID: Trust in Public Health Information During a Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "096Z", "7367", "7914" ], "program_officials": [ { "id": 3564, "first_name": "William", "last_name": "Bainbridge", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2022-04-30", "award_amount": 194774, "principal_investigator": { "id": 3567, "first_name": "Kenneth R", "last_name": "Fleischmann", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3565, "first_name": "Bo", "last_name": "Xie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 3566, "first_name": "Min Kyung", "last_name": "Lee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This research in crisis informatics will provide evidence of how the general population is reacting to the COVID-19 pandemic and share the findings broadly with healthcare workers to help them limit the negative effects on society and encourage citizens to take appropriate actions to reduce the spread of the disease. Over the longer term, the results will contribute to improvements in our preparedness for future pandemics. It will increase our understanding of the role that social media plays in public understanding of and response to a pandemic, and also will advance work on message framing within health informatics and health communication. Some of the results could help society and government prepare for potential contingencies such as bioterrorism and other unexpected large-scale emergencies. During a global health crisis, public health interventions such as quarantine and isolation can limit the spread of the virus, reducing morbidity and mortality while reducing the impact on the health care system. Health education interventions, particularly via social media, are critical for ensuring that the general public heeds recommendations from experts. Given the generally low e-health literacy of older adults, and in light of the increased impact that pandemics have on older adults, as is particularly the case for COVID-19, ensuring that older adults and their caregivers understand and trust public health information is critical for saving lives.This mixed-method study will allow quick and safe collection of data from people currently experiencing a pandemic, to understand the factors that influence trust in current public health information interventions in a real setting. Its online data collection will not risk the health of researchers or participants, seeking a stratified sample including a significant number of older adults and employing a robust recruitment strategy. The work will be done in three rigorous but swift stages: (1) a survey, including a battery of previously validated instruments along with open-ended questions specific to the current COVID-19 pandemic; (2) an experiment to compare the effectiveness of different health messages; and (3) a set of recommendations that can guide public health officials in deciding how to tailor messages for particular audiences based on factors such as age and e-health literacy. Human subjects perform differently in real versus hypothetical scenarios, so the best time to evaluate how people react to public health interventions during a pandemic is when a very significant one is in process. COVID-19 represents a rare opportunity to collect data from people while they are experiencing the phenomenon under investigation. This project will identify factors that influence trust in public health information, and how public health information interventions can be tailored to be effective for people of various ages.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": "1629", "attributes": { "award_id": "2032125", "title": "RAPID: Human Sound Localization and Analytics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4280, "first_name": "Alexander", "last_name": "Sprintson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2022-05-31", "award_amount": 100000, "principal_investigator": { "id": 4281, "first_name": "Lili", "last_name": "Qiu", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 is spreading at an unprecedented rate resulting in the death of so many people all over the world. Social distancing is so far the most effective method to limit its spread. However, manually enforcing social distancing is not only labor-intensive but also error-prone and even dangerous due to possible physical contact. This project proposes to develop techniques and mobile systems that localize human sound such as cough and voice and alarm a user when someone is within the social distance. If successful, this work will significantly advance the state-of-the-art in wireless sensing and localization. To maximize the impact, the researchers will collaborate with industry and local community and release software to the public. The research outcome will also be incorporated into the graduate and undergraduate curriculum. The proposed research aims to develop algorithms and systems to localize uncontrolled and unknown human sound. A unique advantage is that it does not require cooperation from other phones and whoever uses it can immediately benefit from it. It exploits the phone mobility as the user moves to enable localization. The multi-resolution analysis will be performed on low-frequency voice signals to further enhance accuracy. When another phone is cooperating, it will further leverage the time of flight between the two phones to improve the performance.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": "1764", "attributes": { "award_id": "2033846", "title": "RAPID: Dual COVID-19 and Influenza Virus Detection via Target Antibody-Functionalized Graphene Field-Effect Sensing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "090E", "096Z", "104E", "7914" ], "program_officials": [ { "id": 4639, "first_name": "Svetlana", "last_name": "Tatic-Lucic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2021-05-31", "award_amount": 150000, "principal_investigator": { "id": 4641, "first_name": "Deji", "last_name": "Akinwande", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4640, "first_name": "Andrew D", "last_name": "Ellington", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Both COVID-19 and Influenza are infectious respiratory diseases, and their symptoms are hard to distinguish at the early stage. Influenza, commonly known as the flu, spreads through the world in yearly outbreaks, mainly in the fall-winter season, and causes up to 650,000 death per year with 3-5 million severe cases. COVID-19, emerged recently, has already been positively detected in almost 4 million individuals worldwide. Due to the absence of effective, low-cost, point-of-care detection methods, the number of affected individuals is estimated to be, perhaps, 10- fold higher. While scientists, doctors, and whole nations are hard at work to resolve the pandemic, there exists a pressing and urgent need for diagnostics that can differentiate between COVID-19 and influenza. The situation is such that a second wave of the coronavirus spread will likely appear in the fall/winter of 2020, which also coincides with the seasonal outbreak of influenza. Therefore, it is of rapid importance to develop a dual specific and selective biosensor for direct confirmation of the presence of influenza and/or COVID-19 virus within the patient’s body fluids, such as saliva. Authors of the work propose to utilize ultrasensitive graphene nanomaterial transistors, that are functionalized with the antibodies as the virus-specific biosensors. The researchers aim to build a sensor explicitly designed towards the detection of virus bodies directly. Successful completion of this project will lead to an early-stage diagnosis tool that differentiates between influenza and COVID-19, yet embodied into a single device, which is crucial for containing the forthcoming outbreak in order to promote public health.The investigators intend to approach the problem of virus detection by building a specific dual bioelectronic sensor that employs electrolyte-gated graphene-based field-effect transistors. Among other potential detection methods and transducers, electrolyte-gated graphene field-effect transistors are known for their extremely high sensitivity to analytes. Besides, graphene is biocompatible, stable in ambient, and inert in the aqueous environments. The graphene channel will be functionalized with specific antibodies through a 1-pyrenebutanoic acid, succinimidyl ester (PASE) linker molecule. PASE will facilitate the particular conjugation with both COVID-19 specific, and influenza-specific antibodies. Building upon the direct PASE-antibody conjugation, the researchers plan to explore another, more robust and modular path, which employs conjugation of graphene with ssDNA and use the antibody:compDNA hybrids to improve the antibody attachment onto graphene. The particular conjugation of graphene-to-SARS-CoV-2 and graphene-to-H1N1 specific antibodies will be studied within the scope of the work. In order to test the COVID-19 and Influenza biosensors, the spike protein (S1) and H1 hemagglutinin protein (H1 HA) will be used as analytes, respectively. Based on the protein biosensing, knowledge on the biosensor’s detection limit, their sensitivity, dynamic range, accuracy, and response time will be obtained. Following the dual conjugation of two graphene channels with specific antibodies, one additional channel will be chemically passivated in order to provide a reference signal and allow for the removal of non-specific signals. The inactivated SARS-CoV-2 and H1N1 viruses will then be used to explore the real-world application properties of the built biosensor, exploring specificity and selectivity of such a sensor, cross-reactivity, and throughput. The specific passivation will enable reliable signal recording at the next stage of virus detection, using real body fluid samples to assess the biosensor. Combining a specific and dependable biomolecular integration of both SARS-CoV-2 and H1N1 viruses with graphene’s electrical readout will enable an accurate, specific, and rapid test for the two deadly respiratory diseases.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": "1920", "attributes": { "award_id": "2037261", "title": "RAPID: Collaborative Research: The Transformation of Essential Work: Managing the Introduction of AI in Response to COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5096, "first_name": "Andruid", "last_name": "Kerne", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2022-09-30", "award_amount": 86373, "principal_investigator": { "id": 5097, "first_name": "Samantha", "last_name": "Shorey", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Millions of people deemed “essential workers” in the COVID-19 pandemic perform manual labor, such as sorting, cleaning, garbage collection, and recycling. To mitigate risks associated with this work, there is an accelerated push to introduce artificial intelligence (AI) to safeguard the public and workers from disease transmission. Yet, decades of human-computer interaction and organizational communication research shows that the introduction of new technologies into workplaces is not an easy transition; instead technologies transform and displace existing work practices. This research project investigates both beneficial innovations and liabilities arising in waste management industries, as they deploy AI technologies in response to the COVID-19 crisis. It develops a set of best practices for the coordination of human labor and AI to address the pandemic, transforming the future of work. The best practices will be presented as guidance on how to incorporate AI into critical economic institutions to mitigate the negative effects of COVID-19 on public health, society, and the economy. This guidance will regularly be communicated to workers, industry leaders, and the public through an open access toolkit, a workshop series, press releases, and social media. This will potentially benefit essential industries that employ or serve tens of millions of workers, including waste labor, shipping, manufacturing, retail, and food service.This project will be conducted through a multi-site ethnographic study, examining how two American waste management organizations negotiate the introduction of automated technologies, in an effort to mitigate risks associated with the COVID-19 pandemic. The first involves automated “floor care” robots at Pittsburgh International Airport. The second involves AI sorting systems in a single stream recycling plant, in Austin, Texas. By studying two sites, the research team is expected to gain comparative insight into how automation is introduced and attuned, according to professional, regional, and institutional norms. Data collection will include ethnographic fieldnotes, interview transcripts, and media materials. Extending theories of technological diffusion and invisible labor, the research team will qualitatively analyze the technology dissemination process, drawing insights from the actions and perspectives of workers as they negotiate the changing shape of their daily work. Through reflexive memos and “constant comparative” coding, the research will identify patterns of action and build a set of transferable observations. This is expected to yield (1) empirical findings on factors that promote or hinder rapid technological introduction in response to crisis, with specific insights on the human labor required to make automated technologies work (e.g., calibration, troubleshooting, and maintenance), (2) theoretical findings that contribute core understandings of the diffusion of innovation and how workplace technologies are reinvented through use, and (3) design recommendations for a variety of essential work sectors.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": 1383, "pages": 1424, "count": 14236 } } }