Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&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=1392&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=-keywords" }, "data": [ { "type": "Grant", "id": "10130", "attributes": { "award_id": "2051440", "title": "REU Site: Research Experience for Undergraduates in Genomics and Biochemistry", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "RSCH EXPER FOR UNDERGRAD SITES" ], "program_reference_codes": [], "program_officials": [ { "id": 1044, "first_name": "Sally", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-01", "end_date": "2024-02-29", "award_amount": 328827, "principal_investigator": { "id": 11950, "first_name": "Fern", "last_name": "Tsien", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1160, "ror": "", "name": "Louisiana State University Health Sciences Center", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1160, "ror": "", "name": "Louisiana State University Health Sciences Center", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "This REU Site award to the Louisiana State University Health Sciences Center, located in New Orleans, LA, will support the training of 10 students interested in genomics and/or biochemistry research for 10 weeks during the summers of 2021-2023. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities or from groups underrepresented in the sciences, will be trained in the program. All projects are hypothesis-driven, focusing on solving scientific problems and utilizing computational state-of-the-art scientific methods. Students will analyze and interpret data and communicate their findings. They will develop a competitive resume, receive training in potential careers in STEM, and contribute new knowledge in genomics and biochemistry. Student projects will be flexible for remote and/or on-campus work. By the end of the program, students of diverse backgrounds will become more competitive in their graduate school applications and research careers. Students will learn how research is conducted, and many of them will present the results of their work at scientific conferences. \n\nThe program will focus on one-on-one training in genomics and biochemistry, emphasizing hard scientific skills (hypothesis and experimental design, methodology, scientific communication), soft skills (time management), and professional development (responsible conduct in research, resume/CV writing, career guidance). REU students will have networking opportunities with current graduate students, post-doctoral fellows, and faculty members. They will have the opportunity to enhance their scientific communication thru interaction with inner-city K-12 schools. The goals of this program are to facilitate training in data analytics and scientific visual literacy, to provide learning platforms for current datasets and research problems, and enable trainees to use these materials in a variety of situations (e.g., a pandemic). Computational and wet lab projects adaptable to remote applications will be available based on student research interests. Assessments of the program will be performed through the online SALG URSSA tool. Students will be tracked after the program in order to follow their career paths. More information about the program is available by visiting http://www.medschool.lsuhsc.edu/genetics/reu.aspx , or by contacting the PI (Dr. Fern Tsien) at [email protected] or [email protected].\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": "10131", "attributes": { "award_id": "2036161", "title": "SBIR Phase I: Development of a novel technology to manufacture animal-free muscle proteins in bacteria and yeasts", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-15", "end_date": "2022-05-31", "award_amount": 256000, "principal_investigator": { "id": 26033, "first_name": "Chenfeng", "last_name": "Lu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1888, "ror": "", "name": "FYBRAWORKS FOODS INC.", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial impact of this Small Business Innovation Research (SBIR) Phase I project includes improved food security, environmental benefits, and human health benefits. This project will develop a meat alternative that more closely mimics the taste and texture of animal meat. The technology developed from the proposed project will be used to build a vertically integrated food manufacturing platform that can withstand supply chain disruptions from natural disasters and pandemics. These meat alternatives will reduce and replace traditional meat consumption, with many environmental benefits - lowered greenhouse gas emissions and aquatic pollution; lower use of energy, water and land; and reduced antibiotics usage. There are benefits to human health for reduced meat consumption. Furthermore, production costs will be eventually be comparable to that of mushroom farming.\n\nThe proposed project aims to develop a fermentation-based meat alternative that more closely mimics the taste and texture of animal meal through recombinant protein technologies. Additionally, the project aims to leverage the texture and flavor of mushroom mycelia, and supplement this with recombinant muscle proteins to further enhance the taste and nutritional profiles and overcome many of the shortcomings of existing plant-based meat products. To date the concept of combining recombinant muscle protein and single cell protein is novel and has not been reported. Large gaps persist between plant-based and lab-grown meat, with regards to cost and consumer experience. Plant-based meat is more affordable but faces consumer resistance due to sub-optimal texture and nutritional profiles, while cultivated meat offers a consumer experience similar to that of animal meat but at a much higher cost. Towards this goal, muscle protein genes will be expressed in a microbial host and enzymatically cross-linked with vegetable protein to produce protein fibers that can be formulated into synthetic meat. The technical objectives also include demonstration the feasibility of crosslinking muscle fiber proteins extracted from meat with mycoprotein from mushroom to produce desired textural and flavor properties.\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": "10132", "attributes": { "award_id": "2104223", "title": "I-Corps: A contactless, non-intrusive, artificial intelligence (AI)-enabled contact tracing system for reducing the spread of viruses", "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": "2021-02-01", "end_date": "2023-04-30", "award_amount": 50000, "principal_investigator": { "id": 26035, "first_name": "Mona", "last_name": "Azarbayjani", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26034, "first_name": "Hamed", "last_name": "Tabkhi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 248, "ror": "https://ror.org/04dawnj30", "name": "University of North Carolina at Charlotte", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is to use recent advances in artificial intelligence (AI) and deep learning to enhance public health challenges in nursing homes. Fever, as a non-specific measure of infection, is commonly observed in a broad range of diseases and pandemics. The proposed AI-powered assessment system will create an effective, non-intrusive tool for empowering nursing home facilities and clinics to combat the spread of contagious diseases and future pandemics, all the while providing a higher health resiliency for our communities. The proposed technology creates a real-time health surveillance system that also may be adopted and customized to a wide range of public health applications that require continuous, non-intrusive health monitoring with predictive analytics and proactive decision making. The proposed research has significant opportunities both in the public and private sectors.\n\nThis I-Corps project is based on the development of a monitoring system to mitigate the risk and control the spread of epidemic viruses through real-time artificial intelligence, multi-sensor fusion, and video data analytics. In contrast to existing approaches that have a narrow focus with limited intelligence capabilities, the proposed technology offers a holistic solution to enable scalable, reliable symptom assessment and contact tracing from a distance with strict personal privacy measures ensured. By utilizing both red green blue (RGB) and thermal cameras (off-the-shelf products), it may be possible to provide a more precise system that is capable of monitoring several health indicators simultaneously; e.g., body temperature, respiratory rate, coughing, and sneezing while taking a non-intrusive approach. The proposed device is equipped with an AI-enabled contact tracing system for reducing the spread of viruses by identifying the potentially infected individuals at the early stage. For privacy-aware contact tracing, the plan is to leverage previously developed technology for real-time privacy built-in human pose estimation, re-identification, trajectory analysis, and activity recognition. The technology creates lightweight, end-to-end execution of real-time computer vision based on RGB cameras, with the ability to perform at a high frame rate on embedded and edge devices.\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": "10133", "attributes": { "award_id": "2046102", "title": "CAREER: An Algorithm and System Co-Designed Framework for Graph Sampling and Random Walk on GPUs", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software & Hardware Foundation" ], "program_reference_codes": [], "program_officials": [ { "id": 2785, "first_name": "Almadena", "last_name": "Chtchelkanova", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-01", "end_date": "2026-02-28", "award_amount": 584001, "principal_investigator": { "id": 26036, "first_name": "Hang", "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": 1113, "ror": "https://ror.org/02z43xh36", "name": "Stevens Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "Graph analytics is one of the key technologies to address the grand challenges of our time, such as understanding the spread of pandemics, designing extremely large-scale integrated circuits and uncovering software vulnerabilities among many others. However, as the size of the graph continues to grow, learning, mining and computing such gigantic graphs become ineffective, impractical, and potentially dire. Fortunately, Graph Sampling and Random Walk can dramatically reduce the size of the original graphs, while still capturing the desired properties for downstream graph analytics tasks. But a comprehensive system that can perform graph sampling and random walk on real-world trillion-edge graphs at an acceptable speed is absent. This research pioneers the effort of uniting various graph sampling and random walk algorithms behind a user-friendly framework that can take advantage of world-class Graphics Processing Unit (GPU) computing facilities, including the future exascale ones, to rapidly handle trillion-edge graphs. This project contributes to the U.S. national goal of increasing participation in science and engineering, which is crucial to America’s success in addressing global challenges, building a stronger and more diversified workforce, and meeting the needs of the global innovation economy. This project produces a high-performance software library that serves as a foundational tool for fellow science and engineering practitioners from academia, national laboratories and industry. With a commitment to helping K-12, undergraduate, female, and Underrepresented Minority (URM) populations in the Science, Technology, Engineering, and Mathematics (STEM) field through the interesting investment and rewarding education plan, this project lays out a comprehensive road map to prepare the next-generation high-performance graph analytics professional workers and researchers. This project revamps and creates core courses in both graduate and undergraduate levels for the PI's home department. To benefit the society at large, this project disseminates the project data, software, and publications to the broader research community at http://personal.stevens.edu/~hliu77/gsrw.html.\n\nThe overarching goal of this research is to make graph sampling and random walk fast, scalable and user-friendly. Towards that end, this career proposal advocates algorithm and system co-designed researches. First, this research introduces novel update and construction designs for transition probability of various major Monte Carlo methods that are essential for fast sampling. Second, to fully unleash the potential of GPUs, this project formulates the key primitive into problems that can take advantage of general, and reserved tensor and ray tracing cores on GPUs. Third, based upon the asynchronous processing nature of graph sampling and random walk, this research exploits Remote Direct Memory Access (RDMA)-assisted task and partition adaptive scheduling mechanism to reduce the data transfers for scalable trillion-edge graph sampling and random walk. Last but not the least, this career research delivers a bias-centric framework, which offers end users expressiveness to program not only a variety of exiting GSRW algorithms but also future ones, and simplicity by hiding the aforementioned advanced optimization techniques.\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": "10134", "attributes": { "award_id": "2049645", "title": "REU Site: Physics and Astronomy at the University of Minnesota", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "Integrative Activities in Phys" ], "program_reference_codes": [], "program_officials": [ { "id": 1997, "first_name": "Kathleen", "last_name": "McCloud", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-15", "end_date": "2024-02-29", "award_amount": 378239, "principal_investigator": { "id": 26037, "first_name": "Alex", "last_name": "Kamenev", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "This site is supported by the Department of Defense in partnership with the NSF REU program.\n\nThis is an award for the Research Experience for Undergraduates (REU) program at the University of Minnesota’s Twin Cities campus School of Physics and Astronomy. It will enable ten undergraduate physics and astronomy majors to take part in cutting edge research by participating in a ten-week summer program. Each student will work in a research group, supervised by a faculty member, making the transition from classroom instruction to state-of-the-art research, from mastering old knowledge to creating new experiences. The project enhances opportunities for development and education for the involved students, on the one hand, and advances the progress of science, on the other. An additional focus emphasizes diversity, the participation of students from underrepresented groups, and first-generation college students. The majority of participants will come from smaller colleges, where they do not have the opportunity to participate in front-line research during the regular academic year. By encouraging students to participate in scientific research, particularly students from non-Ph.D. granting institutions, the summer REU program addresses a critical national need for skilled employees in Science, Technology, Engineering, and Mathematics (STEM) fields.\n\nThe specific projects will be in astrophysics, high-energy physics, condensed matter, and biophysics. Most of the student projects are anticipated to be in the area of experiment and data analysis. Some projects may focus on theoretical aspects of the areas listed above. In case the in-person participation proves to be problematic due to COVID19, the project leaders have prepared a log of projects that may be executed remotely with on-line interactions between participants and advisors. Most of such projects are in the area of data analysis of astrophysical or LHC-generated data.\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": "10135", "attributes": { "award_id": "2037673", "title": "Porous silicon on paper-based optical biosensor for diagnostics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EPMD-ElectrnPhoton&MagnDevices" ], "program_reference_codes": [], "program_officials": [ { "id": 1160, "first_name": "Usha", "last_name": "Varshney", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-04-01", "end_date": "2024-03-31", "award_amount": 375000, "principal_investigator": { "id": 26039, "first_name": "Sharon", "last_name": "Weiss", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26038, "first_name": "Paul E", "last_name": "Laibinis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 189, "ror": "https://ror.org/02vm5rt34", "name": "Vanderbilt University", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "There is a critical need for the development of cost-effective, highly sensitive, widely deployable, rapid diagnostic testing systems that can be adapted for detecting a variety of pathogens. This type of testing system can give healthcare providers key information necessary to make educated treatment decisions, and also can facilitate epidemiological studies of disease distribution patterns. This project investigates whether incorporation of a high surface area porous nanomaterial on a paper substrate can enable a new, highly sensitive, quantitative, and reliable platform for rapid diagnostic testing. The porous nanomaterial serves as the active sensing region that produces a clear optical signal change when signature molecules are selectively captured inside the pores, while the paper substrate is a simple and cost-effective fluid delivery vehicle that enables active transport of a test solution to the porous nanomaterial. The proposed work will lead to advanced understanding of fluid flow dynamics and molecular attachment in porous nanomaterials integrated with paper as well as advanced knowledge related to interfacing nanomaterials with non-traditional substrate materials. Educationally, this program will expose students to interdisciplinary research at the intersections of optics, materials science, engineering, and chemistry. A hands-on optical biosensor demonstration kit will be developed and deployed to K-12 students through shareable videos, classroom visits, and on-campus outreach activities. \n\nThe goal of this project is to demonstrate a porous silicon-on-paper optical biosensor capable of rapid, accurate, quantitative, and high sensitivity detection of protein biomarkers that will significantly advance the capabilities of rapid diagnostic testing. A comprehensive understanding of the achievable performance metrics, tolerances, and potential limitations of the porous silicon-on-paper optical biosensor platform will be attained. To accomplish this goal, key advances to realize porous silicon in a lateral flow configuration on paper and understand the fluid flow dynamics and molecular binding kinetics in such a configuration will be achieved. Specifically, this project seeks to: (1) develop a robust approach for integrating nanoscale porous films with paper-based microfluidic substrates; (2) understand molecular transport and binding kinetics in a porous film-on-paper platform as a function of the chemical and physical characteristics of the porous film and species infiltrated in the film; (3) establish the viability of using porous silicon optical thin films in a lateral flow rapid test framework; and (4) validate sensor through detection of SARS-CoV IgM and IgG antibodies.\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": "10136", "attributes": { "award_id": "2115149", "title": "RAPID: Decentralization and Privacy for Secure Vaccination Coordination", "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": 1213, "first_name": "James", "last_name": "Joshi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-04-01", "end_date": "2023-03-31", "award_amount": 200000, "principal_investigator": { "id": 2106, "first_name": "Ramesh", "last_name": "Raskar", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 210, "ror": "https://ror.org/042nb2s44", "name": "Massachusetts Institute of Technology", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 210, "ror": "https://ror.org/042nb2s44", "name": "Massachusetts Institute of Technology", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The objective of this project is to develop and deploy a privacy-protecting, user-centric, digital solution to enhance vaccination coordination and to create a privacy-preserving, data-aggregation platform for researchers. The project will enable a decentralized, end-to-end protocol that spans the entire vaccine user journey, from enrollment in phased vaccination to long term monitoring of adverse effects. The team will consider health equity from the perspective of trust, privacy, and inclusivity. Current systems for vaccination coordination focus on a single part of the system or require a smartphone for every vaccine recipient which aggravates the equity concerns. This project addresses such concerns through an array of user-facing solutions: QR codes on paper vaccination cards which can operate offline as well as mobile phone apps without live internet access. The standardized data sharing system consolidates both population-wide and individualized information in a single platform to increase the speed and effectiveness of the intervention, vaccination in this case, so that it can be monitored and analyzed. This enables a bird’s eye view of the cyber-physical-social ecosystem without creating a surveillance state. The project will push the boundaries of data-driven predictive analytics for pandemic response and pandemic preparedness. \n\n\nThe project’s goal is to provide a user-centric solution that can aid researchers and planners of the current and future pandemics. In this project, the user’s journey and the relevant de-identified data collection is divided into four parts: (i) Digitally enhanced enrollment system for phased vaccination using digitally signed coupons, (ii) A privacy-preserving QR code based vaccination card, and a smartphone app to interface with vaccination sites without revealing any personally identifiable information to centralized servers, (iii) Proof of vaccination in a tamper-evident and secure manner available with digitally signed offline credentials, (iv) Monitoring and alert systems for adverse reactions that enable users to upload their symptoms in a cryptographically authenticated manner. The project involves building data aggregation and data dissemination solutions with varying levels of granularity for population-scale and individual scale analysis. For aggregation, to preserve the privacy of early contributors, the project will use a new generation of techniques based on secure multi-party computation. For dissemination, the project will use Split Learning and Split Inference methods invented by the investigator at MIT that may be able to better address privacy-utility trade-offs. Vaccination data is critical at all three levels: (i) logistics and monitoring of vaccines (ii) vaccination workflows and (iii) user experience before and after vaccination. This project will generate tools for an efficient data gathering monitoring system for future pandemics and emergencies of a similar nature without invasion of personal freedoms. The systems and methods are being built by a consortium of epidemiologists, engineers, data scientists, digital privacy evangelists, professors, and researchers from various institutions. Such a diverse collaboration is essential to minimize disruption to the existing vaccination system and ensure a smooth vaccination roll-out across the nation.\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": "10137", "attributes": { "award_id": "2120050", "title": "Support for Students to Attend 2021 APS-DAMOP Conference", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "AMO Experiment/Atomic, Molecul" ], "program_reference_codes": [], "program_officials": [ { "id": 6612, "first_name": "Kevin", "last_name": "Jones", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-04-01", "end_date": "2023-03-31", "award_amount": 12000, "principal_investigator": { "id": 25754, "first_name": "Subhadeep", "last_name": "Gupta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award provides support for student participation in the 2021 meeting of the American Physical Society's Division of Atomic, Molecular and Optical Physics, expected to be held virtually due to the ongoing pandemic. The award supports waivers of graduate student registration fees for approximately 120 students. The conference program includes many of the research topics central to Atomic, Molecular, and Optical Physics and Quantum Information Science. The support of students through this award makes a substantial contribution to the education and training of future scientists. Students who graduate with a background in atomic, molecular, and optical physics acquire a broad range of knowledge and skills that enable them to contribute to progress in many areas of science and technology. \n\nThe meeting is scheduled to be held May 31 through June 4, 2021. Originally planned to be held in Fort Worth Texas, the meeting will be held virtually due to the ongoing pandemic. The conference offers an opportunity for students to present their research results and to interact with senior scientists primarily from the United States, but also the broader international community. Support is provided only for US students (students enrolled in US universities).\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": "10138", "attributes": { "award_id": "2047064", "title": "CAREER: Toward Artificial General Intelligence for Complex Adaptive Systems: A Natural Concurrent “Learning-in-Learning” Control Paradigm", "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": 1084, "first_name": "Aranya", "last_name": "Chakrabortty", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-15", "end_date": "2026-02-28", "award_amount": 500105, "principal_investigator": { "id": 26040, "first_name": "Zhen", "last_name": "Ni", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 675, "ror": "https://ror.org/05p8w6387", "name": "Florida Atlantic University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "Artificial intelligence (AI) technologies are transforming nearly every aspect of our lives and reinforcement learning (RL) is viewed as one of next big research topics in the current AI wave. While the existing AI and RL achievements are exciting, the fundamental research of data aggregation, learning and approximation capability, and the performance generalization during uncertainties, is not fully yet developed. There is still a gap from the current state-of-the-art techniques to the artificial general intelligence that can bring good performance in learning speed, data efficiency, and generalization of the optimization performance.\n\nInspired by this observation, the PI proposes a natural concurrent RL framework that carries three major advantages over traditional RL methods, namely the i) advantages of simultaneously learning multimodal properties of the complex system; ii) structural advantages of using a personalized learning scheme; and iii) implementation advantages of the data-driven sample-efficient design. Within this framework, the PI proposes to design two concurrent RL methods to consolidate past experiences and anticipatory knowledge and build the “learning-in-learning” control paradigm. The theoretical results will certify that the proposed RL framework can be deployed with high confidence for complex adaptive systems under uncertain environments. The applications on smart energy community will support the novel learning framework and theoretical results.\n\nBeyond the scientific impacts, the proposed research has broader impacts for a wide range of research disciplines including transportation, rehabilitation, and robotics. The integration of research and education activities will also positively impact the institutions regionally and nationally. A proposed workshop will bring world renown experts to engage (state college) students and young researchers with limited financial supports to attend professional conferences. The collaboration with the industry and the national laboratory provides the students the opportunity to get external training, which can lead to competitive job offers. The proposed take-home AI/RL projects will promote interactive distance learning for schools with limited research capacity (e.g., rural community college) and for students with the preference of remote studying during the current pandemic. These activities will vigorously contribute to the nation’s AI workforce development.\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": "10139", "attributes": { "award_id": "2044221", "title": "CAREER: Kinship, Community, and Alternative Networks of Care in Contexts of Precarity", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Cultural Anthropology" ], "program_reference_codes": [], "program_officials": [ { "id": 616, "first_name": "Jeffrey", "last_name": "Mantz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-04-01", "end_date": "2026-03-31", "award_amount": 483051, "principal_investigator": { "id": 26041, "first_name": "Kathryn", "last_name": "Mariner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 464, "ror": "https://ror.org/022kthw22", "name": "University of Rochester", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "In times of social isolation, the importance of social relations comes into sharp relief, particularly for marginalized communities who experience biomedical, environmental, and public upheaval. As communities adapt to uncertainty amid a global pandemic and societal unrest, it is imperative to ask what new cultural forms will arise in the wake of social rupture. This project will examine how alternative networks of care are forged within marginalized communities, and whether they are transformed when stressed by heightened social isolation, precarity, and unrest. The project provides funding for the training of both graduate and undergraduate students in methods of empirical, scientific data collection and analysis, and contributes significantly to broadening the participation of groups historically underrepresented in science. It also builds scientific infrastructure and capacity through the fostering of research collaboration between universities and local communities, and increases public literacy of science and the scientific method by making findings accessible within a range of public settings.\n\nWith the support of a Faculty Early Career Development (CAREER) Award, the researcher will conduct a multi-phase, longitudinal project that explores how African Americans create and navigate space within rapidly changing social, physical, and political landscapes. Research questions addressed will include: What is the relationship between kinship, race, and physical space? How do marginalized individuals build physical spaces of interpersonal connection, sociality, and kinship within a context of marginalization? How might physical spaces produce or transform kinship? The research will unfold in three-phased design that includes archival methods, photography, and community mapping. This project will advance scientific knowledge about human social difference and connection, contemporary urban life, structures of inequality and cooperation, and kinship formation in times of crisis and precarity. The project contributes to theories of racialization, social organization, spatial analysis, and urban social inequality. The research will be paired with a three-year community-based undergraduate curriculum, which will cultivate intergenerational mentorship through collaborative research and skill-sharing.\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": 1384, "pages": 1392, "count": 13920 } } }