Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1394&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1395&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1393&sort=-keywords" }, "data": [ { "type": "Grant", "id": "9966", "attributes": { "award_id": "2144668", "title": "CAREER: Wide Area Wireless Sensing: Theories and Applications", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Networking Technology and Syst" ], "program_reference_codes": [], "program_officials": [ { "id": 710, "first_name": "Murat", "last_name": "Torlak", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-01", "end_date": "2027-04-30", "award_amount": 621984, "principal_investigator": { "id": 25761, "first_name": "Jie", "last_name": "Xiong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 200, "ror": "https://ror.org/0072zz521", "name": "University of Massachusetts Amherst", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).\n\nSensors are omnipresent in our everyday lives. They are embedded in a diverse range of systems such as smartphones, wearables, gaming devices, medical equipment and automobiles. Wireless sensing is an exciting new research area which utilizes existing wireless signals instead of conventional sensors for sensing human beings and the surrounding environment, benefiting a large spectrum of disciplines including elderly care, human–computer interaction, environment monitoring, and disaster response. The contact-free and sensor-free nature of wireless sensing makes it particularly appealing in challenging scenarios such as pandemic and disaster survivor search. This project aims to lay the theoretical foundations for long-range wide-area wireless sensing and to use LoRa signals to realize new applications such as disaster survivor sensing, humidity sensing and gas leakage detection.\n \nIn particular, this project will build the theoretical foundations to uncover the underlying principles of long-range wireless sensing and to guide the design of wireless sensing systems. This project also aims at pushing the performance boundaries of wide-area wireless sensing in terms of range, accuracy, and robustness to enable new applications which were not possible previously. The project comprises several major research thrusts: (i) Develop a general model to quantify the performance of long-range sensing, analyze factors affecting the performance, and propose signal processing schemes to improve sensing performance and deal with interference; (ii) Study the effect of strong secondary reflection in LoRa sensing and leverage the mobility of drones to further increase the sensing coverage; (iii) Enable detection of survivors even in a coma through long-range through-wall respiration sensing; and (iv) Achieve long-range humidity sensing and gas leakage detection by deploying just one LoRa transceiver pair without any additional sensors.\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": "9967", "attributes": { "award_id": "2144348", "title": "CAREER: Advancing Autonomy for Soft Tissue Robotic Surgery and Interventions", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "FRR-Foundationl Rsrch Robotics" ], "program_reference_codes": [], "program_officials": [ { "id": 8531, "first_name": "Siddiq", "last_name": "Qidwai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": "2027-02-28", "award_amount": 599936, "principal_investigator": { "id": 25762, "first_name": "Axel", "last_name": "Krieger", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The research supported by this Faculty Early Career Development (CAREER) grant will make contributions to our fundamental knowledge in robotics and autonomy related to healthcare. Autonomous robotic surgery systems have the potential to significantly improve efficiency, safety, and consistency over current tele-operated robotically assisted surgery. Complication rates of soft tissue surgery such as kidney tumor resections and bowel surgery reach up to 18 and 30 percent, respectively. There is a significant learning curve in robotic surgery, surgical outcomes vary greatly between hospitals, and postoperative complications differ significantly by surgeon. This project will enable the development of a new generation of surgical robots with increasing levels of autonomy that reduce complication rates and improve outcomes independent of surgeon’s experience. This research has the potential to democratize access to the highest level of healthcare by providing consistent expert-level results, mitigate the rising healthcare costs by increasing efficiency, and help in future pandemics by protecting healthcare workers. Thus, results from this research will benefit the US society welfare and economy. The multi-disciplinary research and education activities in robotics and autonomy and related diverse set of intellectual communities ranging from computer vision, sensing, artificial intelligence, to healthcare will help to inspire a diverse set of students from groups that have been traditionally underrepresented in science, technology, engineering, and mathematics (STEM) and to substantially increase participation in robotics research.\n \nPresent day approaches to automated manipulation are unable to emulate highly trained humans in the performance of complex manipulation tasks in varying, unstructured, and deformable environments. These research and experiments on deformable tissue tracking will yield new techniques for identifying and tracking subtle tissue targets in unstructured environments. The research on deformation prediction will produce methodologies for understanding tissue behavior and how to compensate for deformations. The control-design activities of this project will address shortfalls in autonomous robot controllers by providing new strategies maximizing autonomy, while providing fail-safe operation. \n\nThis project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9968", "attributes": { "award_id": "2146490", "title": "CAREER: Optimizing Power Processing for Heterogeneous Energy Storage Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EPCN-Energy-Power-Ctrl-Netwrks" ], "program_reference_codes": [], "program_officials": [ { "id": 25763, "first_name": "Mahesh", "last_name": "Krishnamurthy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-15", "end_date": "2027-01-31", "award_amount": 500000, "principal_investigator": { "id": 25764, "first_name": "Al-Thaddeus", "last_name": "Avestruz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This NSF CAREER project aims to enable widespread and equitable access to sustainable power and energy through sustainable energy storage. Globally, the number of used EV batteries is rising exponentially together with the need for stationary energy storage, which is a concern for sustainability. The project will bring transformative change to the techno-economic feasibility of using retired batteries from electric vehicles (EV) for stationary battery energy storage (BESS) for grid and EV fast charging applications. This will be achieved by dramatically reducing the cost of power processing within a second-use battery energy storage system (2-BESS), which is currently a significant portion of the total cost. The intellectual merits of the project include building the foundation of theory, algorithms, and hardware for a new method for using and optimizing power processing in battery energy storage systems consisting of second-use (2U) EV batteries. The broader impacts of the project include widespread penetration of renewable energy sources while maintaining a more robust grid and reducing the cost of energy, equitable access and energy justice through affordability, availability, and sustainability; faster proliferation of EVs by reducing the cost of fast charging by buffering the energy and reducing the potential cost of grid upgrades and peak demand charges; and providing high-quality and effective, remote/distance learning to a workforce that may be vulnerable to changing technology and manufacturing landscapes, global crises (like pandemics), and shifting socio-economics.\nAlthough second-use (2U) EV batteries contain approximately 80% remaining capacity, the challenge in their cost-effective use is the diversity in their characteristics that include different drive and temperature cycles while in the EV and potentially different chemistries. This results in a significant variation or heterogeneity in their energy and power capabilities, and their degradation rates. The current state of the art in handling the heterogeneity is to use one power converter at the output of every battery, leading to higher cost and power losses. The research in this project will address: (1) a design and optimization framework for power processing with few dissimilar converters at lower power ratings; (2) optimal control of 2-BESS in a new power processing architecture.\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": "9969", "attributes": { "award_id": "2144121", "title": "CAREER: Future phylogenies: novel computational frameworks for biomolecular sequence analysis involving complex evolutionary origins", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Innovation: Bioinformatics" ], "program_reference_codes": [], "program_officials": [ { "id": 2641, "first_name": "Jean", "last_name": "Gao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": "2027-02-28", "award_amount": 585696, "principal_investigator": { "id": 25765, "first_name": "Kevin", "last_name": "Liu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 521, "ror": "https://ror.org/05hs6h993", "name": "Michigan State University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).\n\nPhylogenetics is the discipline that seeks to reconstruct and analyze the phylogeny, or evolutionary history, of a set of organisms. Phylogenetic reconstruction is primarily accomplished through computational analysis of DNA and other biomolecular sequence data. Phylogenies and the evolutionary insights that they provide are essential to biology and other disciplines, as well as many applications: important examples include reconstructing and studying the Tree of Life - the evolutionary history of all life on Earth, understanding human origins, infectious disease epidemiology and discovery of new solutions to future pandemics, crop improvement and agriculture, and forensic science. One of the two key ingredients needed for phylogenetic studies has seen a major leap forward thanks to advances in biomolecular sequencing technology: the scale of available biomolecular data is now among the largest in any domain and, in 2025, biomolecular data velocity and storage is projected to be comparable to or larger than Twitter and YouTube. On the other hand, recent \"big data\" phylogenetic studies point to a critical gap regarding the second of the two key ingredients in phylogenetics: existing computational algorithms need to move beyond their traditional simplifying assumptions about biomolecular sequence evolution. Two of the most important assumptions are: (1) \"sequence-unaware\" methods that ignore the inherently sequential nature of biomolecular sequences, and (2) the pre hoc assumption that evolutionary relationships have a simple branching structure and are \"tree-like\" - i.e., can be accurately described by a tree or other simple representation. New computational approaches and infrastructure are needed to move beyond these traditional assumptions and unlock the study of \"future phylogenies\" and next-generation phylogenetics. This project will therefore create new pathbreaking models and algorithms for complex phylogenetic analyses of biomolecular sequence data. The project also addresses gaps in STEM education through new curriculum development and a collaboration with the Impression 5 Science Center, a children’s science museum in mid-Michigan. Project impacts will be broadened through open-source software distributions and open data resources, new scientific discoveries enabled by the developed software and data infrastructure, scientific outreach activities, and student training and mentoring with a strong emphasis on diversity, equity, and inclusion (DEI).\n\nThis project will advance the field of computational phylogenetics along multiple frontiers. The first research objective is to develop new statistical resampling algorithms that move beyond \"uninformed\" analysis where biomolecular data are assumed to be independent and identically distributed (i.i.d.), and towards \"informed\" sequence-aware analysis; a central approach will be to make use of the latest advances in machine learning. The new algorithms will be used to better assess rigor and reproducibility during phylogenetic analyses and other critical-path analytical tasks. The second research objective is to create mathematical theory, statistical models, and computational algorithms to move beyond traditional phylogenetic representations (e.g., phylogenetic trees, etc.), and towards more general graph-theoretic models of complex genome evolution. The third research objective is to conduct comprehensive validation and performance assessment studies of the first two research objectives’ computational frameworks. The studies will utilize both synthetic and empirical benchmarking datasets that capture a wide range of evolutionary conditions and dataset features. The project also includes two educational objectives: a new course on DEI topics in interdisciplinary computer science, and a new museum exhibit on technology and computer programming that will be exhibited at the Impression 5 Science Center. Open-source software and open data deliverables will drive future methodological research and enable otherwise inaccessible scientific discoveries, and scientific outreach will help seed and drive uptake of the project’s contributions. The project also includes student training and mentoring activities at the undergraduate and graduate levels. Project deliverables and other results can be found at https://gitlab.msu.edu/liulab.\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": "9970", "attributes": { "award_id": "2146530", "title": "CAREER: Catastrophic Rare Events: Theory of Heavy Tails and Applications", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "OE Operations Engineering" ], "program_reference_codes": [], "program_officials": [ { "id": 2155, "first_name": "Georgia-Ann", "last_name": "Klutke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2027-03-31", "award_amount": 568493, "principal_investigator": { "id": 25766, "first_name": "Chang-Han", "last_name": "Rhee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 317, "ror": "https://ror.org/000e0be47", "name": "Northwestern University", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national prosperity and welfare by developing mathematical tools that provide strategies to understand and mitigate risk associated with the \"heavy-tail\" phenomena. Heavy-tailed distributions provide useful mathematical models for seemingly disparate rare events, such as the global pandemic, the 2012 blackout in India, and the 2007 financial crisis. Beyond such isolated catastrophic events, heavy tails are pervasive in large-scale complex systems and modern algorithms. A particularly simple and well-known manifestation of heavy tails is the so-called “80-20 rule”, whose variations are repeatedly discovered in a wide variety of application areas. Under the presence of heavy tails, high-impact rare events are guaranteed to happen eventually, and may occur more frequently than decision-makers may account for. Accounting for (or even utilizing) the impact inflicted by such rare events will support the design and operation of reliable and resilient systems in many important scenarios, including environmental catastrophes, power system failures, financial crises. The accompanying educational plan aims to broaden STEM interest in underrepresented communities and train future leaders of academia, industry, and government by equipping them with fundamental skills in risk analysis.\n\nThis research will develop a comprehensive theory of large deviations and metastability for heavy-tailed stochastic systems. The classical theory of large deviations and rare-event simulation has a long history but these approaches and the metastability framework often fall short when the underlying uncertainties are heavy-tailed. This project leverages and extends recent advances in extreme value theory, optimization, control, and stochastic simulation to fill the gap by building large deviations and metastability frameworks tailored for heavy-tailed systems. With the new framework, the project will also address open problems in artificial intelligence and actuarial science. This research will contribute to a rigorous theoretical foundation for designing reliable and accountable AI so that the technology can be applied to high-stake decision-making problems. Successful implementation of such a program will expand our understanding of how system failures and phase transitions arise in many stochastic systems, which, in turn, will provide provably efficient computational machinery for insurance risk management and accountable AI design.\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": "9971", "attributes": { "award_id": "2149555", "title": "REU Site: Injury Science Research Experience for Undergraduates", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EWFD-Eng Workforce Development" ], "program_reference_codes": [], "program_officials": [ { "id": 2240, "first_name": "Amelia", "last_name": "Greer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2025-08-31", "award_amount": 392542, "principal_investigator": { "id": 25768, "first_name": "Flaura", "last_name": "Winston", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25767, "first_name": "Thomas", "last_name": "Seacrist", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 687, "ror": "", "name": "The Children's Hospital of Philadelphia", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Injuries and violence remain the leading causes of death and acquired disability for children, youth, and young adults in the U.S. and worldwide. The Injury Science Research Experiences for Undergraduates (REU) Site at the Children’s Hospital of Philadelphia’s Center for Injury Research and Prevention (CHRP) provides a personalized internship experience for a diverse pool of talented interns with the aim of inspiring the pursuit of careers in science, technology, and engineering. This Site immerses interns in a 10-week rigorous, interdisciplinary program of mentored scholarly research, professional development, and graduate school preparation that results in a broadened awareness of STEM career opportunities. The overarching goal is to provide research training designed to encourage and enable students from STEM-limited schools and those who are underrepresented in STEM to pursue advanced degrees, and ultimately, careers in science and engineering. Students benefit from an increase in knowledge, develop interest in science and engineering, gain professional development, develop the ability to solve real-world problems, and prepare to optimize technology for safety solutions through user-centered, participatory design and human factors research. \n\nTraffic injuries and violence have substantially increased, fueled by societal changes and the pandemic. This requires innovative ideas and an expanded workforce focused on injury science to effectively develop and implement novel prevention strategies. The Injury Science Research Experiences for Undergraduates (REU) Site at the Children’s Hospital of Philadelphia’s Center for Injury Research and Prevention (CHRP) provides a personalized internship experience for a diverse pool of talented interns with the aim of inspiring the pursuit of careers in science, technology, and engineering. This REU site renewal project focuses on the leading injury risks to children – traffic crashes, sports-related injury, and violence. Students will have access to and engage in research projects with faculty using large, unique datasets through data science; leverage large clinical exposure of human factors engineering (to optimize safe driving and care delivery during crises); employ neuroscience preeminence for bench-to-bedside translational science (animal models, virtual reality and magnetoencephalographic assessments, and head impact sensors); and interact with a large and diverse clinical population to advance pediatric rehabilitation. A cohort of 8 interns each year will engage in Injury Science to advance research and training innovations that elucidate injury mechanisms and develop novel prevention strategies and technologies as the future injury science workforce. New for this renewal are increased recruitment levels of racial/ethnic minorities from STEM-limited schools and increased participation from Historically Black Colleges and Universities (HBCUs) through nationwide on-site HBCU recruitment visits and dedicated positions for HBCU students.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9972", "attributes": { "award_id": "2134796", "title": "Concealing Infectious Disease", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Social Psychology" ], "program_reference_codes": [], "program_officials": [ { "id": 2516, "first_name": "Steven", "last_name": "Breckler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2025-03-31", "award_amount": 525000, "principal_investigator": { "id": 25769, "first_name": "Joshua", "last_name": "Ackerman", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "Sickness is a common experience, yet people do not always tell others when they are sick with an infectious illness. Failing to disclose, or actively hiding, illness presents clear risks to individual, public, and even organizational health. For instance, pre-pandemic surveys indicate that up to 90% of U.S. employees still go to the workplace when sick, and productivity losses from the common cold alone are estimated at close to $25 billion annually. To help better understand decisions to conceal infectious disease, this project addresses two main themes: (1) What individual and social factors are associated with the concealment of sickness? For example, is concealing illness more common in specific types of relationships or by people with certain personality traits? Does it depend on whether the illness is particularly dangerous or not? (2) What are the psychological consequences of making this decision? For example, does the mental effort involved in concealing sickness interfere with people’s thinking and problem-solving abilities, does it impair their social relationships, and does it trigger feelings of guilt and shame that might contribute to antisocial tendencies?\n\nTo test these questions, one set of descriptive studies measures how often people hide sickness and what motivates those decisions depending on who is being concealed from (friends, coworkers, strangers) and features of the illness being concealed (transmission risk, harm severity, illness longevity). In a second set of experiments, study participants are enrolled when they report actually becoming sick with an infectious illness. The focus here is whether concealing one’s illness causes changes in cognitive functioning (intrusive thoughts, performance under stress), social relationships (feeling connected to and valuing others), and moral judgment (self-identification of dishonesty, guilt/shame). Finally, psychological processes associated with infection concealment are compared against processes associated with concealment of non-infectious conditions, such as stigmatized identities and secrets, to help determine whether concealing sickness is a unique behavior requiring unique solutions. The project informs future efforts to mitigate public health challenges associated with the spread of disease and associated workplace challenges. It also provides a basis for improving the welfare of infected individuals by advancing knowledge about perceived barriers to revealing one’s condition and the resulting cognitive and affective impact when one does not reveal.\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": "9973", "attributes": { "award_id": "2150697", "title": "I-Corps: Flavonoid derivative for treatment of anxiety without alcohol or opiate enhancing properties", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": "2022-08-31", "award_amount": 50000, "principal_investigator": { "id": 25772, "first_name": "James", "last_name": "Simon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25770, "first_name": "Ariane", "last_name": "Vasilatis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25771, "first_name": "Eileen", "last_name": "Carry", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the development of safe and effective treatments for anxiety, specifically for individuals at high risk of substance use disorders and polysubstance overdoses. Benzodiazepines (BZDs) are the most commonly prescribed class of anti-anxiety medication, and currently prescribed to more than 5% of the US population. Although effective, BZDs are highly addictive and commonly involved in polysubstance overdose fatalities. In fact, BZD prescription rates per state are directly linked to overdose fatality rates. Despite this risk, BZDs remain widely prescribed, even in those at high risk for polysubstance use. When BZDs are taken with alcohol, opiates, or other CNS depressants, they can cause life threatening respiratory depression among other effects such as drowsiness and reduced motor function, which often lead to accidents and hospitalizations. The proposed research is committed to the development safer and effective alternatives to BZDs, specifically for those at risk of polysubstance overdose. In doing so, the goal to improve safety and efficacy of anxiety treatment and substance use disorders, improve long-term recovery success, and combat the rising pandemic of polysubstance overdose fatalities. \n\nThis I-Corps project is based on the development of partial and functionally subtype selective GABAA (Gamma-Amino Butyric Acid-A) receptor modulators for the treatment of anxiety, specifically for individuals at high risk of substance use disorders and polysubstance overdoses. Select natural and synthetic flavonoids have been shown to be potent GABAAR modulators with anxiolytic activity and reduced sedative and alcohol-enhancing effects. However, flavonoids are poorly absorbed and rapidly excreted due to a lack of druglike properties. This has led us to the design of novel derivatives with improved druglike properties with the aim of identifying a druglike anti-anxiety (GABAAR PAM) without alcohol and opiate enhancing properties. The foundation for this project is based upon work completed in 2021 targeting positive modulation and inhibition ethanol-induced potentiation of GABAARs as a novel mechanism for the treatment of alcohol use disorder. This work also led to the filing of a provisional patent, and a recent publication.\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": "9974", "attributes": { "award_id": "2210185", "title": "EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Studying Social Engineering Attacks Targeting Vulnerable Refugee Populations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Secure &Trustworthy Cyberspace" ], "program_reference_codes": [], "program_officials": [ { "id": 704, "first_name": "Daniela", "last_name": "Oliveira", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": "2024-06-30", "award_amount": 296470, "principal_investigator": { "id": 25774, "first_name": "Mythili", "last_name": "Menon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25773, "first_name": "Murtuza S", "last_name": "Jadliwala", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 931, "ror": "https://ror.org/00c4e7y75", "name": "Wichita State University", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true }, "abstract": "Two recent world events, an ongoing pandemic together with continuing regional wars and conflicts, have resulted in a sizable number of refugee population being forcibly displaced and resettled in the United States. Lack of English-language and digital literacy skills have made the refugee community an easy target for new and increased social engineering attacks. This project’s overarching goal is to study how language and linguistic features impact the perception and response of refugees to social engineering attacks. The project’s novelties are a) a language- or linguistic-centric approach for understanding social engineering threats to refugees resulting in development of novel educational materials, open access datasets, and improved cybersecurity policies to counter these threats, and b) tailored digital literacy and educational programs and workshops to raise awareness and mitigate social engineering threats among the refugee population. The project’s broader significance and importance are a) improved protection models and prevention techniques against social engineering attacks, b) informed policies and tools, resulting in better social and economic security, and reduced inequity gap within the vulnerable refugee population, and c) enhanced refugee integration into the U.S. society through increased awareness of cybersecurity threats. \n\nThe project team systematically study the linguistic cues and triggers associated with social engineering attacks such as phishing and vishing. The deliverables and key contributions of the project include a) preliminary results documenting the impact of linguistic traits of the refugee population on their susceptibility to real-world social engineering attacks, b) open-access datasets of phishing emails, and voice calls, with attributions of interactions to promote further research, c) recommendations to improve cybersecurity policies including vulnerable population, resulting in the creation of new STEM educational material for the refugee population, and d) follow-up educational workshops to debrief, discuss, and disseminate relevant educational material to the target refugee population.\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": "9975", "attributes": { "award_id": "2154564", "title": "Random Structures and Dynamics Arising from Questions in Social, Biological, and Physical Sciences", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "PROBABILITY" ], "program_reference_codes": [], "program_officials": [ { "id": 2955, "first_name": "Pawel", "last_name": "Hitczenko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-01", "end_date": "2025-06-30", "award_amount": 249814, "principal_investigator": { "id": 25775, "first_name": "Shirshendu", "last_name": "Chatterjee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 538, "ror": "", "name": "CUNY City College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Analysis of random structures and stochastic dynamics has been a topic of interest across many disciplines. Study of various spatial and non-spatial random graph models, stochastic spatial models, and stochastic processes taking place on random graphs, and network-based algorithms are key to these efforts. Although these problems have received significant attention in the literature, rigorous analysis and foundational work is needed in many areas to have reliable results and to cope with upcoming challenges and the complexity of real-world data. This research addresses several theoretical and some empirical challenges involving a wide class of questions arising in physics, social sciences, and biosciences. Specific research areas that would benefit from this research include determining functional connectivity within neural networks in brains, pandemic management, pest control strategies, and the evolution of social interactions. This research will provide new insights into the mechanistic underpinning of the underlying complex structures and associated covariates on different social and biological phenomena which are the central objective of research in many disciplines. This analysis will help scientists to understand experimental and observational data in biosciences and social sciences and develop suitable control strategies. Community detection for temporal networks would enhance the understanding of brain function across multiple spatial and temporal scales. The proposed work also has significant potential to increase our capacity to develop efficient and cost-effective intervention strategies to mitigate some of the most potentially damaging and fastest-spreading invasive epidemics of humans, livestock, and plants (including wheat, soybean, citrus, and hop). To guarantee that the results are relevant to scientists, parts of the project will be carried out in collaboration with scientists of different fields. Presentations on the relevant research will be given in stakeholder meetings and community outreach events. Undergraduate and graduate students will be involved in the research activities at the intersection of mathematics and other disciplines. Opportunities to learn theoretical and empirical analysis of various probabilistic models, and interactions with scientists will enhance their ability to work at the interface between mathematics and various related areas. \n \nOne of the primary goals of this project is to understand and analyze different structural properties and limiting behavior of various spatial and non-spatial random graph models, and several stochastic dynamics taking place on graphs. The random graph models include open clusters of percolation and related models, multi-layer, and temporal network models. Percolation models originated in the physics literature as a model for a porous medium. There are many useful tools and a well-developed theory for studying the percolation models on two-dimensional lattices. However, for higher dimensional lattices, several key aspects, including the near-critical regime and the behavior of the model in subgraphs such as sectors, are poorly understood. This project will address some of these issues. Multi-layer networks are natural models for numerous datasets arising in various scientific fields, including genomics, biomedical sciences, neuroscience, economics, sociology, ecology, epidemiology, and technological networks. Depending on the context, various parametric probabilistic models have been used for the formation of multi-layer, multiplex, and temporal networks. For the estimation of those parameters from data, and for model selection purposes, it is very important to understand the behavior of some key functionals (e.g., subgraph counts or the concentration of aggregated adjacency matrices). These functionals will be analyzed for a wide variety of temporal network models, particularly where the snapshots have some correlation structure. The stochastic dynamics include some important variants of the standard models for infection spreading and opinion evolution in presence of additional restrictions (e.g., temporary isolation). The project also includes studying several theoretical and empirical aspects (e.g., bounds for detectability threshold, efficient detection algorithms) of various detection problems, which includes detection of anomalous structures, community detection from a correlated sequence of networks, detection of the source of the epidemic. The rigorous analyses of the models discussed above will require the development of novel mathematical techniques and research tools. These techniques and tools would be instrumental to obtain a deeper understanding of a broader class of random structures and dynamics arising in multiple scientific disciplines.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1394, "pages": 1419, "count": 14184 } } }