Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1383&sort=-principal_investigator
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-principal_investigator", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1382&sort=-principal_investigator" }, "data": [ { "type": "Grant", "id": "646", "attributes": { "award_id": "2050058", "title": "Facilitating Rapid and Actionable Responses to Social, Behavioral, and Economic-Related COVID Questions: The Societal Experts Action Network (SEAN)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1472, "first_name": "Robert", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-10-31", "end_date": "2022-10-31", "award_amount": 996358, "principal_investigator": { "id": 1473, "first_name": "Emily", "last_name": "Backes", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 339, "ror": "https://ror.org/038mfx688", "name": "National Academy of Sciences", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 339, "ror": "https://ror.org/038mfx688", "name": "National Academy of Sciences", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has disrupted nearly every aspect of life across the globe. As decision makers at the federal, state, and local level respond, they are grappling with numerous complex scientific questions. Many of these questions are grounded in the social, behavioral, and economic (SBE) sciences. Questions such as:• What strategies are most likely to restart economic growth in critically important sectors that were heavily affected by COVID-19?• How can governments more effectively encourage use of masks and other strategies to reduce disease transmission?• What recent knowledge will allow public and private sector educational entities to improve on-line learning outcomes?The Division of Behavioral and Social Sciences and Education at the National Academies of Sciences, Engineering, and Medicine, in collaboration with the National Science Foundation, continues the work of the Societal Experts Action Network (SEAN) which was established during the summer 2020 in response to the COVID pandemic. SEAN products are designed to provide actionable responses to urgent policy questions asked by federal, state, and local decision makers. SEAN is unique in its focus on rapid, readable, and research-based insights in response to questions on issues such as the reopening of businesses and economic growth, the education of children, the mental health and resilience of our communities, and many more. The resulting products are made publicly available and widely disseminated, which benefits not only the requesting official, but a broad range of decision makers and the public. The value of SBE sciences in addressing problems of national importance is shared with an expansive audience, thus contributing to our nation’s overarching understanding of how best to deploy SBE knowledge in pandemic and other crisis situations.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": "9986", "attributes": { "award_id": "2213343", "title": "Crime Rates during the Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Law & Science" ], "program_reference_codes": [], "program_officials": [ { "id": 1420, "first_name": "Reginald", "last_name": "Sheehan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": "2023-04-30", "award_amount": 49998, "principal_investigator": { "id": 1473, "first_name": "Emily", "last_name": "Backes", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 339, "ror": "https://ror.org/038mfx688", "name": "National Academy of Sciences", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 339, "ror": "https://ror.org/038mfx688", "name": "National Academy of Sciences", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "Recent upticks in certain crimes, such as homicide, has garnered a great deal of public attention and media coverage, with little nuanced discussion of data, explanations, or possible policy implications. The increase in violent crime has sparked widespread concern but little rigorous discussion over causes or data needs. Crime and public safety is of critical importance to federal, state, and local officials, and interjecting critical analysis based in data and social and behavioral science will contribute to our nation’s understanding of how best to shape policy responses to crime increases during the pandemic with an emphasis on data and research needs for rigorous policy evaluation.\n\nThe proposed workshop will review the available data and analyses of changes in multiple offense types during the pandemic. Key issues will include: the strengths and limitations of existing data, differing results depending on the source and type of data, and the association between crime rate changes and pandemic-related restrictions on population mobility. The activity will explore existing explanations, focusing on the social and behavioral theories that inform them, the types of crime to which they do and do not apply, and the types of data needed to test them. The proceedings resulting from the proposed workshop will be made publicly available and widely disseminated, which will benefit not only researchers and policymakers, but a broad range of criminal justice stakeholders and the public. Such knowledge will also help to identify future basic research and data needs.\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": "645", "attributes": { "award_id": "2102905", "title": "RAPID: Partisanship, Trust, and Vaccine Hesitancy: Impacts of the 2020 Election on COVID-19 Risk Management", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1466, "first_name": "Robert", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-11-15", "end_date": "2022-10-31", "award_amount": 198846, "principal_investigator": { "id": 1471, "first_name": "Rob A", "last_name": "DeLeo", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 338, "ror": "https://ror.org/01px48m89", "name": "Bentley University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1467, "first_name": "Katherine L", "last_name": "Dickinson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1468, "first_name": "Jennifer D", "last_name": "Roberts", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1469, "first_name": "Elizabeth", "last_name": "Koebele", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1470, "first_name": "Lindsay", "last_name": "Neuberger", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 338, "ror": "https://ror.org/01px48m89", "name": "Bentley University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The United States is on the verge of two potentially watershed moments in the fight against COVID-19. First, COVID-19 vaccines are being tested in clinical trials, suggesting widespread distribution as early as spring 2021. Vaccination is widely considered one of the most critical public health interventions for curbing the spread of COVID-19, which has already claimed more than 220,000 American lives. At the same time, the 2020 U.S. presidential election is likely to have a significant impact on vaccine uptake given the hyper-politicization of both the pandemic and vaccines broadly. All of this is occurring against the backdrop of heightened attention to structural racism, which has identified significant disparities in COVID-19 impacts across racial groups. The confluence of these events will have profound implications for the long-term trajectory of the COVID-19 pandemic in the U.S., as well as for the public’s perceptions of vaccine risk and government trustworthiness. Based on data collected through a multi-wave national survey of U.S. residents, this project explores the impact of factors such as partisanship, risk perceptions related to vaccines, and other exogenous—and often unpredictable—events on intended uptake of the COVID-19 vaccine. The results of this study advances key theories of risk management, information seeking, and the policy process in the context of novel risks. The research also provides timely and usable information to public health officials about the design of equitable policies and practices for bridging the gap between vaccine availability and uptake.In the context of COVID-19, the period between the 2020 U.S. election and the expected approval of a COVID-19 vaccine provides an opportunity to assess how exogenous events in the political and information environment shape individuals’ vaccine-related perceptions and behaviors. Using a three-wave panel survey distributed to a demographically-representative national sample of U.S. residents, this study captures changes in risk perceptions and behavioral intentions, as well as in factors such as partisanship, trust in institutions, and structural racism that may influence vaccine uptake. The first wave of data collection (T1) is immediately following the announcement of 2020 election results. Because presidential elections tend to magnify polarization while garnering enormous media attention, this timing represents a critical moment to capture initial data for the project. Subsequent survey waves are approximately February 2021 (T2) and April 2021 (T3) as vaccine development advances. This research produces findings that promote a more robust understanding of the process through which individual risk perceptions evolve across time and interact with social and political factors to influence behavior.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": "644", "attributes": { "award_id": "2055251", "title": "RAPID: Flexible, Efficient, and Available Bayesian Computation for Epidemic Models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1464, "first_name": "Katharina", "last_name": "Dittmar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-11-15", "end_date": "2021-10-31", "award_amount": 187014, "principal_investigator": { "id": 1465, "first_name": "Andrew E", "last_name": "Gelman", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Decisions about coronavirus response are necessarily based on statistical models of prevalence, transmission risks, case fatality rate, projection of future spread of infection, and estimated effects of medical and social interventions. Much of this modeling and inference is being done using the Bayesian framework, an approach to statistics that is well suited to integration of information from different sources and accounting for uncertainty in predictions that can be input into decision analysis. This is a project to develop computing tools to make Bayesian methods more accessible to researchers in quantitative social science and public health who are studying COVID-19 and epidemic models more generally. This work promises to advance scientific knowledge by enabling researchers to fit more flexible and realistic models accounting for multiple sources of uncertainty in data, and to advance societal goals by facilitating more accurate and granular estimates of exposure, reproduction rate, and other aspects of epidemic spread that inform public and private decision making. This project also provides professional development opportunities for a post-doctoral researcher, as well as student training.The research will be done in the open-source programming language Stan, which has already been used in several influential COVID-19 models as well as in economics, political science, biology, political science, and many other application areas. Specifically, the project includes: documentation and language features to make Stan programs easier to write and evaluate; continuation and extensions of existing collaborations on mathematical models for epidemic spread, causal models for estimating policy effects, and survey adjustment; and improved implementations for differential-equation models, which serve as the core of models for disease transmission and other diffusive social and biological processes.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": "643", "attributes": { "award_id": "2034140", "title": "Tunnel Junction Based AlGaN Ultraviolet Lasers", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1461, "first_name": "Dominique", "last_name": "Dagenais", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-01", "end_date": "2023-11-30", "award_amount": 399999, "principal_investigator": { "id": 1463, "first_name": "Shamsul", "last_name": "Arafin", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 308, "ror": "", "name": "Ohio State University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1462, "first_name": "Siddharth", "last_name": "Rajan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 308, "ror": "", "name": "Ohio State University", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true }, "abstract": "While visible blue lasers have been demonstrated with excellent performance, realizing ultraviolet lasers at wavelengths shorter than 300 nm is still challenging despite efforts over more than a decade by various research groups. The principal problem for electrically-powered lasers is related to achieving a reasonable electrical p-conductivity in laser materials through which electrical current will flow. This makes it challenging to operate lasers at a reasonable electrical power. This project proposes an innovative approach that utilizes tunnel junctions (TJs) which alleviates the p-conductivity problem of laser materials without sacrificing optical performance of the device. These short-wavelength ultraviolet lasers are recently found to be useful for sterilizing surfaces or objects, as one of the precautionary steps to prevent the global spread of the coronavirus (COVID-19). Arguably, this application could be enabled by light emitting diodes (LEDs) in this wavelength regime. However, energy-inefficient LEDs achieved to-date are large, complicated, and expensive, which essentially limits their applicability in these key areas. In addition to high impact research advancement, this project will also support interdisciplinary education activities in nanoscience and nanotechnology. Because the proposed research project crosses different disciplines of science and engineering, such as optics, materials science, electrical engineering, physics, and chemistry, it will lead to a range of potential, hands-on learning activities that can engage students of varying backgrounds. In addition, the scientific insights and technological advances stemming from the research will also broadly impact the field of photonics by enabling operation in this underdeveloped spectral region. There is a tremendous need for electrically-pumped (EP) and continuous-wave (CW) operating AlGaN-based diode lasers in the ultraviolet (UV)B (320280 nm) and UV-C (280–200 nm) wavelength regimes due to a wide range of emerging applications including plant growth lighting, water sterilization, trace gas sensing, curing polymers, and stimulating the formation of anti-cancerogenic substances. The primary objective of the proposed research is to design and demonstrate tunnel-injected EP and CW-operating UV lasers with wavelengths of emission ranging from 320 nm to 280 nm. P-type doping and formation of low-resistive p-ohmic contacts are the key challenges for electrically-pumped UV laser diodes. This work proposes to use novel interband tunnel junctions for ultra-wide band gap AlGaN up to 70% aluminum composition in order to overcome this principal challenge. The work performed within this project will generate new fundamental knowledge on the AlGaN-material system and its several important properties including refractive index, carrier interband tunneling through band-tail states and bandgap narrowing, as well as absorption in ultra-thin layers with quantum confinement. The proposed research involves a novel device concept to realize such highly demanding light sources based on the ultra-wide band gap materials. The device knowledge gained from this research will establish a foundation for demonstrating laser devices with emission in the entire deep-UV spectral regime. The early stage of the project aims to demonstrate broad-area Fabry-Pérot lasers using high-bandgap nAlGaN cladding regions on both sides of the active region. The devices will then be tested in pulsed mode, which will help determine various unknown material properties of high Al composition structures. In the second stage of the project, this project aims to demonstrate application-suited CW-operating lasers by employing narrowridge structures with optimized epitaxial structures. The radically new approach proposed here will enable new scientific understanding in the areas of ultra-wide band gap materials and optical devices as well as could establish the platform for a new class of AlGaN-based UV laser technology.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": "642", "attributes": { "award_id": "2031056", "title": "SBIR Phase I: Rapid Antigen-based SERS assay for COVID-19 Detection (COVID-19)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1459, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-01", "end_date": "2021-08-31", "award_amount": 255468, "principal_investigator": { "id": 1460, "first_name": "Joel S", "last_name": "Tabb", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 337, "ror": "", "name": "Ionica Sciences", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 337, "ror": "", "name": "Ionica Sciences", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact and commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to allow testing of potential COVID-19 patients using a rapid and highly accurate diagnostic test, particularly for patients who are asymptomatic. This will provide a significant advantage in “flattening the curve” of the number of cases by preventing these patients from inadvertently infecting their family and community members. Current protocols require days to return a result, creating problems for public health. This test, the first of many that can be produced using the underlying platform technology, would improve: 1) the ability to rapidly identify patients with active COVID-19 cases for expeditious clinical intervention, reducing transmission by that patient; and 2) outcomes because of the higher performance and accuracy. This Small Business Innovation Research (SBIR) Phase I project addresses the lack of rapid, accurate testing for COVID-19 in near patient settings. This effort will develop an infectious disease platform that combines: 1) DNA aptamers, a recognition element for target proteins; 2) surface enhanced Raman scattering (SERS), a vibrational spectroscopic detection method; 3) and orthogonal partial least squares differential analysis, a well-established statistical method often applied to vibrational spectroscopy-based analyses. By employing aptamers that target SARS-CoV-2 related proteins (e.g. the spike (S) protein), this assay is anticipated to identify the presence of this protein under 30 minutes after oro- or naso-pharyngeal sample is collected, and is ultimately expected to achieve >95% clinical sensitivity and specificity.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": "641", "attributes": { "award_id": "2036240", "title": "SBIR Phase I: REAL-TIME SELF-MONITORING SYSTEM FOR COVID-19 PROGNOSIS", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1457, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-01", "end_date": "2021-11-30", "award_amount": 256000, "principal_investigator": { "id": 1458, "first_name": "Andie K", "last_name": "Conching", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 336, "ror": "", "name": "HAWAII INTEGRATED ANALYTICS LLC", "address": "", "city": "", "state": "HI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 336, "ror": "", "name": "HAWAII INTEGRATED ANALYTICS LLC", "address": "", "city": "", "state": "HI", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to rapidly generate personalized data regarding immune health and exposure to SARS-CoV-2. Detection of SARS-CoV-2 exposure is urgently needed to understand viral spread, conduct contact tracing, provide public health recommendations, prepare for hospitalization or critical care emergencies, and safely reduce the need for social distancing. The proposed system will use new technologies to measure chemicals in blood to understand how COVID-19 evolves after exposure. This approach offers improved speed, accuracy, ease of use, cost, and ability to deploy in communities where clinical resources may not be readily accessible. Machine learning can be used to study population-level data to understand the relationship between immune health and COVID-19 severity. The dataset developed herein can improve the reliability of early signs of severe pathological COVID-19 progression, improving both quality of care and efficiency for public health use. This Small Business Innovation Research (SBIR) Phase I project will enable the development of a remote or home-based self-monitoring system to identify anti-SARS-CoV-2 antibodies (IgG and IgM) and inflammatory biomarkers (CRP and IL-6) from finger-stick blood after viral exposure. Specifically, this technology combines serial serology, lateral flow immunochromatographic assays, and novel app-enabled spectrophotometry to evaluate immune health during the course of infection. This approach will provide novel information and data for large-scale analysis and mitigation measures.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": "640", "attributes": { "award_id": "2102166", "title": "EAGER: Rapid Planar PCR for COVID-19 Testing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1455, "first_name": "Leon", "last_name": "Esterowitz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-01", "end_date": "2021-03-31", "award_amount": 46744, "principal_investigator": { "id": 1456, "first_name": "Gregory W", "last_name": "Faris", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 325, "ror": "https://ror.org/05s570m15", "name": "SRI International", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 325, "ror": "https://ror.org/05s570m15", "name": "SRI International", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The goal of this project is to develop the first point-of-contact molecular diagnostic as a tool for COVID-19 testing. The investigator envisions a point-of-contact test being performed before someone passes through a door, checkpoint, airport gate, or border. Many of the critical strategies for this work have already been established, including the feasibility of performing a one-step assay on various viruses, including SARS-CoV-2, the virus responsible for COVID-19. This project provides a basis for expanding the capability of the method/diagnostic tool, with the goal of establishing a reliable method for detecting COVID-19 positivity from saliva in less than 2 minutes at a cost of approximately $2/test. When functional, this tool could be used routinely to test for hidden spreaders of disease at airports, entrances to hospitals or long-term care facilities. Similarly, the method could be used to screen employees when they arrive to work at health care facilities or other large facilities to protect essential workers and patients. Finally, the method could be used for routine screening at large facilities such as factories, food processing or distribution facilities, and large government buildings, allowing our economy to return to a more normal state. The research will also provide training for a postdoctoral fellow and an undergraduateThis project focuses on establishing a new method for PCR (Polymerase Chain Reaction, a method used to rapidly make millions of copies of a DNA sample) that will realize the goal of a point-of-contact molecular diagnostic test for envelope viruses such as COVID-19. Already established for this work are critical strategies: (1) for rapid, uniform cycling using optical heating; (2) for large scale partitioning to accelerate sample preparation without micro-patterning or microfluidics; and (3) for performing a one-step lysis/rp-PCR (rapid planar-PCR) assay on envelope viruses. The Research Plan is organized under three objectives: (1) to extend the limited area heating due to use of individual LED/lens pairs to large areas through the design and fabrication of a printed circuit that will allow higher density of LEDs per zone, allow many more zones, simplify connections to the rest of the control circuitry, and allow use of surface mount LED drivers; (2) to show, once uniform, large area heating is established, that the diffusion limitation works to provide a very large level of partitioning, with a goal of achieving 1,000,000 partitioning sites in a 5 x 5 cm sample; and (3) to demonstrate the expected assay speed and detection limit (~2 minutes and ~100 copies/milliliter) by modifying reagents, temperatures, and time periods to achieve a good compromise between efficiency of the three steps (lysis, reverse transcription, PCR) overall assay speed, and limit-of detection. If successful, project results will provide the basis for rolling out a point-of-contact test that can be rapidly translated for clinical applications.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": "639", "attributes": { "award_id": "2039945", "title": "D-ISN: TRACK 2: Collaborative Research: Financial Network Disruptions in Illicit and Counterfeit Medicines (FIND-M)", "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": 1451, "first_name": "Wendy", "last_name": "Nilsen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-08-31", "award_amount": 185999, "principal_investigator": { "id": 1454, "first_name": "Nikos", "last_name": "Passas", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1452, "first_name": "Mansoor M", "last_name": "Amiji", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1453, "first_name": "Ravi", "last_name": "Sundaram", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Counterfeit and illegal drugs cause mortality and morbidity for millions of people around the world, as well as damage brands, undermine competition and the rule of law, cause economic losses and security threats, and corrupt financial systems. In light of the global coronavirus pandemic, there is an urgent need to develop a multipronged approach, including access to critical data, network analysis, distributed inference, identification of strategic points of intervention, and mitigation measures to disrupt the flow of counterfeit and illegal medicines in both high and low income countries. Identifying chokepoints (similar to other distribution networks) to effectively disrupt illegal medical supply chains is going to be an important feature of the project. If a solution to this challenge is not found, then prevention and enforcement successes will be partial, illegal entrepreneurs will adapt their modus operandi to circumvent controls, and public health, revenue, fair competition, justice, and security concerns will remain largely unaddressed. This Disrupting Operations of Illicit Supply Networks (D-ISN) planning has the potential to refine questions and solutions that can transform the national, state, and community-level discussions around illegal and counterfeit medicines. This collective effort will introduce a new governance and social control model whereby government, private sector, and academic parties are motivated to share skills, knowledge, and data to tackle the important social problems instigated by illicit entrepreneurs and criminal networks. The goal of this planning grant - bringing together stakeholders from academic, law-enforcement, public and private sectors - is to develop a distributed data infrastructure, populate this infrastructure, and conduct exploratory research in order to leverage financial, commercial and business data, along with previous best practices (from human trafficking and trade-based money laundering controls) for effective disruption of illegal medical and pharmaceutical supply chains. We aim to create robust approaches that will prevent or minimize the social harm caused by these illicit networks and we will coordinate novel, cutting edge efforts to improve outcomes for those victimized. Our specific objectives are to: 1) assemble the stakeholders and partners from other research communities to identify criminogenic asymmetries in the illicit supply networks of counterfeit and illegal drugs; 2) develop a task force, build out the infrastructure, and a detailed plan on how to mine distributed data (financial, business, commercial) using explainable machine learning methods to infer information needed to generate the multiplex networks; 3) stand up a task force, build multiplex networks that capture links discovered by mining the financial, business and commercial data, and develop a detailed research plan on how to discover the “weak-links”; 4) develop a task force, design mitigation strategies, and perform exploratory research on testing the products indicated by our analysis. By design, the project's hypotheses are broad at this stage, in order to incorporate inputs from the diverse stakeholders and partners, and to narrow down the focus during the planning stages of the project. Partners include representatives from trade, public health, and anti-counterfeiting teams both national and international. This research will be informed by the latest work in the area and specific scholars will be asked to join the academic team. Initially, the team will work with historical data, but there is a plan to work with several large financial institutions to run the algorithms we develop in a distributed, secure and privacy-preserving manner on current and live sources.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": "638", "attributes": { "award_id": "2039800", "title": "Experiences that Shape Undergraduate Computing Trajectories: A Equity-Focused Longitudinal Study at Center for Inclusive Computing Institutions", "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": 1448, "first_name": "Jeffrey", "last_name": "Forbes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-10-01", "end_date": "2023-09-30", "award_amount": 1640400, "principal_investigator": { "id": 1450, "first_name": "Linda J", "last_name": "Sax", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1449, "first_name": "Kathleen J", "last_name": "Lehman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Momentum: Accelerating Equity in Computing and Technology research program at the University of California, Los Angeles will conduct a study to produce knowledge that can guide departments’ decisions about recruitment and retention in computing, especially with regard to ensuring equitable structures and opportunities for students who have been historically minoritized. While responding to workforce demands for trained computer scientists, undergraduate computing programs are also increasing their focus on cultivating opportunities for participation among women and students of color (specifically, students who identify as Black or African American, Hispanic or Latinx, Native American, Native Hawaiian or Pacific Islander). The urgent need to address equity issues in undergraduate computing departments is more salient now than ever before, as U.S. colleges and universities currently face a turbulent road to navigate operations amid and after the coronavirus pandemic. This study will provide timely data that can inform undergraduate computing’s recruitment and retention efforts in the current contexts. In collaboration with Northeastern University’s Center for Inclusive Computing (CIC), the Momentum team will survey first- and second-year students who enroll in computing courses at approximately 20 colleges and universities in the spring of 2021. This survey will ask a variety of questions ranging from students’ backgrounds and precollege experiences to their computing experiences in the 2020-21 academic year. Then, they will survey these students again in the spring of 2023 to assess how their experiences with computing have changed and the role these experiences play in shaping their future plans. The project team will analyze these data to understand the specific experiences that shape students’ trajectories in computing, especially the pathways of women and students from historically minoritized groups. The findings from this study will be disseminated widely within the computing and higher education community to inform best practices to recruit and retain students in computing majors and ultimately into computing careers.The Momentum research program at the University of California, Los Angeles will conduct a longitudinal study of first- and second-year students who enroll in computing courses at institutions involved in Northeastern University’s Center for Inclusive Computing (CIC). The research represents a partnership between Momentum and CIC to engage in research on collegiate experiences that promote recruitment and retention in computing, particularly for marginalized students in computing such as women of color. Leveraging Momentum staff expertise and survey instruments to address key gaps in our knowledge, the project team will first administer a baseline survey to first- and second-year students enrolled in computing courses at CIC institutions. This survey (administered in spring 2021) will focus on a variety of factors, ranging from students’ background characteristics and pre-college experiences to their transition to college and their first-year experiences in computing. Then, two years later, in students’ third or fourth year in college, the project team will administer a follow-up survey. The follow-up survey will focus on the specific experiences known to shape students’ trajectories in computing, including course experiences, interactions with instructors/faculty, and extracurricular computing experiences (e.g., involvement in computing organizations, undergraduate research, computing-related internships). This survey will also focus on students’ longer-term plans, such as pursuing graduate school or careers in computing. This study will contribute to knowledge about broadening participation in computing in several ways. First, it will provide insights to scholars, administrators, and policymakers about how students who take computing courses in the first- and second- year of college may engage with the computing department and how this engagement may differentially shape the trajectories of students from various gender and/or racial/ethnic groups. Additionally, following up with these students longitudinally will enable us to learn how these students’ experiences and perceptions change over-time, as well as to study the longer-term role played by a variety of computing environments and outcomes. Overall, this study aims to build on existing literature about what works to promote desirable outcomes for computing students and provide more data on how and why certain experiences work (or do not work), thereby providing actionable findings to stakeholders designing interventions to promote broadening participation efforts in computing.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": 1419, "count": 14184 } } }