Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=5&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=1442&sort=keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=6&sort=keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=4&sort=keywords" }, "data": [ { "type": "Grant", "id": "1281", "attributes": { "award_id": "2027744", "title": "RAPID/Collaborative Research: High-Frequency Data Collection for Human Mobility Prediction during COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "036E", "042E", "096Z", "1576", "7914" ], "program_officials": [ { "id": 3296, "first_name": "Daan", "last_name": "Liang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2021-04-30", "award_amount": 22399, "principal_investigator": { "id": 3297, "first_name": "Qi", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 has and is continuing to dramatically alter the lives of millions of Americans as businesses, schools, and many public places have closed around the country. Recommendations of public officials along with individual concerns and fears have fundamentally changed the pattern of daily routines as Americans have adopted the practices of social distancing, sheltering in place, and even self-quarantine. This Rapid Response Research (RAPID) project will improve our ability to assess and predict changes in mobility patterns under sudden disruptions caused by large-scale public health crises such as COVID-19. The specific focus will be to understand changes in mobility patterns and the complex and dynamic decision-making process shaping these changes during the unfolding events associated with this major public health crisis. The project will advance the national health, prosperity, and welfare by greatly improving the preparedness and responses of public agencies facing COVID-19 and future similar public health crises. It will also help understand and predict reduction, change, and recovery of human mobility patterns promoting the progress of science in human mobility and urban resilience, in alignment with the mission of NSF.The objectives of this RAPID project are to: (1) capture and ultimately predict spatiotemporal changes in the patterns of human mobility in response to the COVID-19 pandemic using social media data mining techniques; (2) perform high-frequency individual-level surveys via a smartphone app to understand motivational, decisional, and sentimental factors shaping changes in mobility patterns; and (3) explore conversion and convergence functions for high fidelity and high accuracy human mobility prediction. The intellectual merits of this research include: the discovery of unique mobility patterns emerging from this public health crises related to social distancing, sheltering, and self-quarantine practices; the unprecedented gathering of longitudinal evidence about the motivational, decisional and sentimental factors shaping mobility decisions; and the development of innovative algorithms of using a small representative sample for high-fidelity mobility prediction. The data and knowledge gained from the project will enhance future studies on urban mobility, travel demand and resource allocation modeling, and help policymakers assess the response and recovery of major urban metropolitan area facing a devastating disaster such as COVID-19. Project outcomes will be disseminated through the Boston Area Research Initiative (BARI), an inter-university partnership between Northeastern University and Harvard University, and through the MetroLab Network.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": "2304", "attributes": { "award_id": "2025954", "title": "LTER: Coastal Oligotrophic Ecosystem Research", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "LONG TERM ECOLOGICAL RESEARCH" ], "program_reference_codes": [], "program_officials": [ { "id": 6330, "first_name": "Paco", "last_name": "Moore", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-01", "end_date": "2025-02-28", "award_amount": 4750800, "principal_investigator": { "id": 6335, "first_name": "John", "last_name": "Kominoski", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 6331, "first_name": "James", "last_name": "Fourqurean", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 6332, "first_name": "Evelyn E", "last_name": "Gaiser", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 6333, "first_name": "Jennifer S", "last_name": "Rehage", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 6334, "first_name": "Kevin", "last_name": "Grove", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 207, "ror": "https://ror.org/02gz6gg07", "name": "Florida International University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "Coastal ecosystems like the Florida Everglades provide many benefits and services to society including protection from storms, habitat and food for important fisheries, support of tourism and local economies, filtration of fresh water, and burial and storage of carbon that offsets greenhouse gas emissions. The Florida Coastal Everglades Long Term Ecological Research (FCE LTER) program addresses how and why coastal ecosystems and their services are changing. Like many coastal ecosystems, the Florida Everglades has been threatened by diversion of fresh water to support urban and agricultural expansion. At the same time, sea-level rise has caused saltwater intrusion of coastal ecosystems which stresses freshwater species, causes elevation loss, and contaminates municipal water resources. However, restoration of seasonal pulses of fresh water may counteract these threats. Researchers in the FCE LTER are continuing long-term studies and experiments to understand how changes in freshwater supply, sea-level rise, and disturbances like tropical storms interact to influence ecosystems and their services. The science team is guided by a diversity and inclusion plan to attract diverse scientists at all career stages. The team includes resource managers – who use discoveries and knowledge from the FCE LTER to guide effective freshwater restoration – and an active community of academic and agency scientists, teachers and other educators, graduate, undergraduate, and high school students. The project has a robust education and outreach program that engages the research team with the general public to advance science discoveries and protection of coastal ecosystems.\n\nThe FCE LTER research program addresses how increased pulses of fresh and marine water will influence coastal ecosystem dynamics through: (i) continued long-term assessment of changes in biogeochemistry, primary production, organic matter, and trophic dynamics in ecosystems along freshwater-to-marine gradients with a focus on how these affect accumulation of carbon and related elevation change, (ii) meteorological studies that evaluate how the climate drivers of hydrologic presses and pulses are changing, (iii) social-ecological studies of how governance of freshwater restoration reflects the changing values of ecosystem services, and (iv) use of high-resolution remote sensing, coupled with models to forecast landscape-scale changes. A new experimental manipulation will determine drivers and mechanisms of resilience to saltwater intrusion. Data syntheses integrate month-to-annual and inter-annual data into models of water, nutrients, carbon, and species patterns and interactions throughout the Everglades landscape to compare how ecosystems with different productivities and carbon stores respond (maintain, increase, or decline) to short- (pulses) and long-term changes (presses) in hydrologic connectivity. Synthesis efforts will use data from national and international research networks aimed at understanding how chronic presses and increasing pulses determine ecosystem trajectories, addressing one of the most pressing challenges in contemporary ecology.\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": "1537", "attributes": { "award_id": "2028098", "title": "Collaborative Research: RAPID: Maintain Mobility and Reduce Infection Through a Resilient Transit and Micromobility System", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 4010, "first_name": "Bruce", "last_name": "Hamilton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-15", "end_date": "2021-12-31", "award_amount": 132226, "principal_investigator": { "id": 4012, "first_name": "Christopher", "last_name": "Cherry", "orcid": "https://orcid.org/0000-0002-8835-4617", "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": "['http://LEVresearch.com/coronavirus']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 190, "ror": "", "name": "University of Tennessee Knoxville", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4011, "first_name": "Candace", "last_name": "Brakewood", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 190, "ror": "", "name": "University of Tennessee Knoxville", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has caused unprecedented disruptions to health, the economy, and transportation systems in cities. The key strategy to combat growth in the infection rate is through \"social distancing,\" maintaining physical separation from others. This strategy is incompatible with transit systems and ridership in nearly every transit market has plunged, causing agencies to reduce service levels to protect riders and employees. Still, mobility is required in many cities and the ability of cities to rapidly recover from widespread economic shutdown will require improving mobility systems in urban areas. In the past decade, more diverse shared mobility systems have proliferated, potentially improving the resilience and redundancy of the urban transportation system. Most recently, micromobility systems (i.e., bike share and scooter share) have entered cities and supplemented transportation systems in those areas. As a shared personal mobility vehicle, micromobility vehicles have been highly utilized in some markets in the early weeks of the pandemic even as transit ridership has declined. This is an important opportunity to understand how shared micromobility systems complement transit and can support social distancing to limit spread of disease while also facilitating urban transportation recovery. To that end, this study has two main aims. First, the research team will conduct a series of panel surveys for the duration of the pandemic of transit and micromobility users in two medium-sized transportation markets (Nashville and Portland) that have robust micromobility markets. These surveys will query and model mode choice behavior in relation to social distancing requirements, transit captivity, job status, income, activity restrictions, and others. The research team seeks to understand if riders self-regulate transit use and exposure and balance their behavior with perceived risks. The team will use detailed agency and micromobility operator data to track changes in ridership during recovery. The second aim is to project those findings on more aggregate ridership data of transit and micromobility across cities of different sizes and with different COVID-19 infection rates. This will provide detailed understanding of how riders react to pandemic in regard to two main shared modes; transit and micromobility systems. To date there has been little work on the response of transit to disease outbreaks. The 2002/2003 SARS outbreak provides some aggregate evidence of long-duration drops in transit ridership. This work will provide important insight into disaggregate rider behavior, disease transmission, and personal- and community-risk tolerance. Moreover, this work will explore how emergent modes, like shared micromobility provide opportunities to maintain mobility while reducing close personal contact. Models developed can help predict how city transit systems will recover after a public health crisis. Public health emergencies will continue into the future and preparation on how to react to such emergencies will be important. For example, micromobility systems may provide much needed transportation services to supplement limited transit service. Understanding the dual roles of micromobility and transit will assist in limiting disease transmission and recovering the transportation system. Policy responses could be based on evidence generated in this study for the current and future infectious disease pandemics.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": "12544", "attributes": { "award_id": "2229975", "title": "Collaborative Research: CyberTraining: Pilot: Operationalizing AI/Machine Learning for Cybersecurity Training", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CyberTraining - Training-based" ], "program_reference_codes": [], "program_officials": [], "start_date": "2023-01-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28473, "first_name": "Houbing", "last_name": "Song", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 315, "ror": "", "name": "Embry-Riddle Aeronautical University", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "The interplay between AI and cybersecurity introduces new opportunities and challenges in the cybersecurity of AI as well as AI for cybersecurity. However, operations and configurations of AI cyberinfrastructure (CI) with a security mindset are rarely covered in the typical AI curriculum. To fill this gap, this project intends to develop hands-on training materials and provide mentored training for current and future research workforce in engineering and science-related disciplines. By transforming and integrating training materials into a course curriculum, this project aims to train potential cyberinfrastructure professionals in the CI community at large to handle AI with and for cybersecurity. This project has the potential to develop the research workforce in operating AI cyberinfrastructure with a security mindset to meet the national and economical needs and priorities of CI advancement. This project’s goal is to broaden the adoption of advanced cyberinfrastructure through training. This project develops a holistic technical approach for cybertraining: to identify, apply, and evaluate AI techniques which are inextricably related to well-defined operational cybersecurity challenges. The project intends to develop a Docker-based training platform that simulates and pre-configures a variety of scenarios to support hands-on AI cyberinfrastructure operations in the context of cybersecurity. Three levels of projects (exploratory, core, and advanced) are designed and integrated into the platform to help researchers and educators customize and develop into different education and training environments. The project democratizes the access and adoption of advanced AI cyberinfrastructure, while integrating cyberinfrastructure skills with the security mindset to foster inter-disciplinary and inter-institutional research collaborations. In addition to the dissemination through publications and social media, the outcomes from this project have the potential to benefit the greater cyberinfrastructure community and beyond, through the training and the sharing of the \"AI for and with cybersecurity\" course curriculum. This project is jointly funded by OAC and the CyberCorps program.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": "14080", "attributes": { "award_id": "2103145", "title": "PostDoctoral Research Fellowship", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "Workforce (MSPRF) MathSciPDFel" ], "program_reference_codes": [], "program_officials": [ { "id": 2352, "first_name": "Stefaan De", "last_name": "Winter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 150000, "principal_investigator": { "id": 30601, "first_name": "Paula", "last_name": "Burkhardt", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2407, "ror": "", "name": "Burkhardt, Paula Elisabeth", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is made as part of the FY 2021 Mathematical Sciences Postdoctoral Research Fellowships Program. Each of the fellowships supports a research and training project at a host institution in the mathematical sciences, including applications to other disciplines, under the mentorship of a sponsoring scientist. <br/><br/>The title of the project for this fellowship to Paula Burkhardt-Guim is \"C^0 Riemannian metrics with synthetic lower scalar curvature bounds and Ricci flow.\" The host institution for the fellowship is New York University, and the sponsoring scientist is Bruce Kleiner.<br/><br/>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": "3585", "attributes": { "award_id": "1663249", "title": "Workshop on Improving the Diversity of Faculty in Electrical and Computer Engineering (iREDEFINE ECE)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EDA-Eng Diversity Activities" ], "program_reference_codes": [], "program_officials": [ { "id": 11638, "first_name": "Christine", "last_name": "Grant", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2017-02-01", "end_date": "2020-01-31", "award_amount": 99856, "principal_investigator": { "id": 11639, "first_name": "Athina", "last_name": "Petropulu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 218, "ror": "", "name": "Rutgers University New Brunswick", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "As a field, Electrical and Computer Engineering (ECE) suffers from a lack of diversity in the participation of women and underrepresented minorities (URM) at the undergraduate, graduate, and professorial levels. This project aims to capitalize on a unique opportunity to bring ECE department heads together with women and underrepresented minority (W-URM) graduate students to help diversify the ECE faculty. The project team, including ECE department heads and the ECE Department Heads Association (ECEDHA) leaders, proposes iREDEFINE, an Annual Workshop focused on increasing the numbers of W-URM holding tenure track faculty or postdoc positions in ECE in the US. iREDEFINE capitalizes on the unique environment provided by the ECEDHA Annual Conference and ECExpo to create a mechanism to motivate and prepare graduate W-URM students for pursuing faculty positions in US universities. The unique opportunity arises due to the high concentration of ECE department chairs attending the event, most of whom are invested in increasing the diversity of their faculty. The two day Workshop will overlap with the ECEDHA Conference. The first day will be devoted to providing a glimpse of the life and career of an ECE faculty, information on different types of schools, tips on how to prepare for a successful academic position interview, information on effective instructional techniques, information on seeking research funding, how to build connections with industry, and how to build confidence and negotiating skills. The second day will coincide with the first day of the ECEDHA conference and all iREDEFINE attendees will have access to all conference sessions. This day will provide an immersive experience on the issues that are on the minds of ECE faculty, such as the latest technology and research trends, the latest teaching innovations, and efforts to find solutions to key problems, such as lack of diversity and shifting enrollment trends. The iREDEFINE attendees will present posters on their work to the ECE department chairs, which will give them the opportunity to closely interact with the very people they will be contacting during their job search and also practice some of the skills that they learned the day before. The poster session will feed into a reception for Workshop attendees, ECE chairs and industry affiliates, which will offer further networking opportunities. In addition to the Workshop, a mentoring program will be established to extend beyond the workshop. The project will be evaluated and fine-tuned after the first offering to maximize its impact.\n\nThis effort will have a significant impact on the field of ECE and as a result the future of technology. Diversifying the faculty will help enhance the diversity of the student population in ECE by providing role models, and thus diversify the ECE workforce. As ECE is a major enabler in technological discovery, a more diverse practitioner/educator pool will lead to new perspectives on technological innovation and how that serves society. The proposed project will serve as a potential model for improving diversity in STEM disciplines in general.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "4097", "attributes": { "award_id": "1639157", "title": "Support for Biennial African School of Fundamental Physics 2016", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "HEP-High Energy Physics" ], "program_reference_codes": [], "program_officials": [ { "id": 13760, "first_name": "James", "last_name": "Shank", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2016-07-01", "end_date": "2017-06-30", "award_amount": 28215, "principal_investigator": { "id": 13761, "first_name": "Jaehoon", "last_name": "Yu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 293, "ror": "", "name": "University of Texas at Arlington", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 293, "ror": "", "name": "University of Texas at Arlington", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This award provides support to enable US. students and lecturers to attend the fourth \"Biennial African School on Fundamental Physics and its Applications\" in Kigali, Rwanda 1-19 August 2016. The schools are based on a close interplay between theoretical, experimental, and applied physics, and Grid computing. They cover a wide range of topics: particle physics, particle detectors, astro-particle physics and cosmology, computing, accelerator technologies, medical physics, condensed matter, light sources and their applications. The participating students are selected from all over Africa and beyond. Scientists from Africa, Europe and the USA are invited to prepare and deliver lectures taking into account the diverse levels of the students. The duration of the school allows for networking -- interactions among students and between students and lecturers. \n\nThis effort will expand the scope of existing collaborations; bringing more African, American and European researchers and students together. An explicit goal of this School is to promote collaboration with Africa. US students and faculty will be involved.\nThis proposal significantly leverages the support of domestic and international organizations including: the NSF (MPS/PHY division and OISE), ICTP, IN2P3, INFN, CERN, DESY, Brookhaven and other National Laboratories and the American Physical Society.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "13568", "attributes": { "award_id": "2144751", "title": "CAREER: Efficient, Dynamic, Robust, and On-Device Continual Deep Learning with Non-Volatile Memory based In-Memory Computing System", "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": 977, "first_name": "Sankar", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-15", "end_date": null, "award_amount": 500000, "principal_investigator": { "id": 8714, "first_name": "Deliang", "last_name": "Fan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 173, "ror": "", "name": "The University of Central Florida Board of Trustees", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "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).<br/><br/>Over past decades, there have existed grand challenges in developing high performance and energy-efficient computing solutions for big-data processing. Meanwhile, owing to the boom in artificial intelligence (AI), especially Deep Neural Networks (DNNs), such big-data processing requires efficient, intelligent, fast, dynamic, robust, and on-device adaptive cognitive computing. However, those requirements are not sufficiently satisfied by existing computing solutions due to the well-known power wall in silicon-based semiconductor devices, the memory wall in traditional Von-Neuman computing architectures, and computation-/memory-intensive DNN computing algorithms. This project aims to foster a systematic breakthrough in developing AI-in-Memory computing systems, through collaboratively developing ahybrid in-memory computing (IMC) hardware platform integrating the benefits of emerging non-volatile resistive memory (RRAM) and Static Random Access Memory (SRAM) technologies, as well as incorporating IMC-aware deep-learning algorithm innovations. The overarching goal of this project is to design, implement, and experimentally validate a new hybrid in-memory computing system that is collaboratively optimized for energy efficiency, inference accuracy, spatiotemporal dynamics, robustness, and on-device learning, which will greatly advance AI-based big-data processing fields such as computer vision, autonomous driving, robotics, etc. The research will also be extended into an educational platform, providing a user-friendly learning framework, and will serve the educational objectives for K-12 students, undergraduate, graduate, and under-represented students.<br/><br/>This project will advance knowledge and produce scientific principles and tools for a new paradigm of AI-in-Memory computing featuring significant improvements in energy efficiency, speed, dynamics, robustness, and on-device learning capability. This cross-layer project spans from device, circuit, and architecture to DNN algorithm exploration. First, a hybrid RRAM-SRAM based in-memory computing chip will be designed, optimized, and fabricated. Second, based on this new computing platform, the on-device spatiotemporal dynamic neural network structure will be developed to provide an enhanced run-time computing profile (latency, resource allocation, working load, power budget, etc.), as well as improve the robustness of the system against hardware intrinsic and adversarial noise injection. Then, efficient on-device learning methodologies with the developed computing platform will be investigated. In the last thrust, an end-to-end DNN training, optimization, mapping, and evaluation CAD tool will be developed that integrates the developed hardware platform and algorithm innovations, for optimizing the software and hardware co-designs to achieve the user-defined multi-objectives in latency, energy efficiency, dynamics, accuracy, robustness, on-device adaption, etc.<br/><br/>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": "1024", "attributes": { "award_id": "2136142", "title": "S2I2: Impl: The Molecular Sciences Software Institute", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [], "program_officials": [ { "id": 2505, "first_name": "Richard", "last_name": "Dawes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": "2026-07-31", "award_amount": 3825190, "principal_investigator": { "id": 2510, "first_name": "Thomas D", "last_name": "Crawford", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 2506, "first_name": "Shantenu", "last_name": "Jha", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2507, "first_name": "Teresa L", "last_name": "Head-Gordon", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2508, "first_name": "Theresa L", "last_name": "Windus", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 2509, "first_name": "Dominika", "last_name": "Zgid", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Molecular Sciences Software Institute (MolSSI) is supported by a joint award from the Divisions of Chemistry (CHE) and Molecular and Cellular Biosciences (MCB) and the Office of Advanced Cyberinfrastructure (OAC). Since its launch in 2016, the MolSSI has served as a nexus for the broad computational molecular sciences community by providing software expertise, community engagement and leadership, and education and training. Through a broad array of software infrastructure projects, teaching workshops, and community outreach, the MolSSI catalyzes the scientific advances needed to solve emerging scientific computing Grand Challenges. In its next phase, supported by this award, the MolSSI will capitalize on this success by continuing and extending its efforts for an even broader impact on our community's ability to address key scientific areas, including developing new energy technologies, fighting COVID-19 and future pandemics, developing climate solutions, exploring quantum information sciences, artificial intelligence and machine learning, and developing a more diverse and resilient workforce.Through the MolSSI's Software Scientists – a team of software engineering experts, drawn from the molecular sciences, computer science, and applied mathematics – the Institute will promote improved interoperability of community codes, easier deployment on heterogenous computing architectures, and greater parallel scalability of existing and emerging theoretical models. The MolSSI will help train the next generation of computational molecular scientists in modern software engineering tools and best practices through its Education Initiative that annually reaches thousands of students worldwide and its Software Fellowship program, which has already benefitted nearly 100 graduate students and postdoctoral fellows across the U.S. The MolSSI's Software Workshop program will bring the community together to identify and address the highest priority challenges and the MolSSI's Discovery, Outreach, and Sustainability programs will provide a key mechanism for community buy-in, provide insight into the needs of the molecular sciences community, and facilitate interactions among its members. The MolSSI's ultimate goal is to enable new science and broader impacts by building a community of molecular scientists prepared to provide solutions to problems impacting national health, social, environmental, and economic challenges.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": "768", "attributes": { "award_id": "2050640", "title": "Planning Virtual Strategies to Prepare Science and Mathematics Teachers in Mississippi", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [], "program_officials": [ { "id": 1805, "first_name": "Susan", "last_name": "Carson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-03-01", "end_date": "2023-02-28", "award_amount": 124992, "principal_investigator": { "id": 1809, "first_name": "Mitchell M", "last_name": "Shears", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 396, "ror": "https://ror.org/01ecnnp60", "name": "Jackson State University", "address": "", "city": "", "state": "MS", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1806, "first_name": "Abu O", "last_name": "Khan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1807, "first_name": "Alicia K", "last_name": "Jefferson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1808, "first_name": "Nadine", "last_name": "Gilbert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 396, "ror": "https://ror.org/01ecnnp60", "name": "Jackson State University", "address": "", "city": "", "state": "MS", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national need for skilled secondary science and mathematics teachers in high-need school districts. To do so, the project seeks to lay the foundation for secondary-education certification programs adapted to the novel demands of pre- and post-COVID teaching/learning environments. Conceived initially as a response to the COVID-19 pandemic, the project aims to use technology and virtual approaches to deliver remote learning opportunities for future teachers. As such, the project will enable Jackson State University to explore the feasibility of a large-scale effort to increase use of evidence-based, distance-learning strategies in teacher education. Examples of strategies include virtual simulations, digital credentialing, and online social and emotional learning. The work will be situated in the urban setting of the Mississippi State capital. This project at Jackson State University includes partnerships with Hinds Community College and Jackson Public Schools, a high-need school district. The long-term goal of this collaborative effort is plan how to recruit, support, and graduate teachers who will help meet the shortage of science and mathematics teachers at high-need schools often staffed by rotating long- and short-term substitute teachers. The project builds on the conceptual framework of Jackson State’s College of Education and Human Development vision of the “responsive educator” who provides and embodies: 1) a Committed Response; 2) a Knowledgeable Response; 3) a Skillful Response; and 4) a Professional Response. Additionally, the project builds on the current infrastructure of the University’s Physics and Mathematics Education curriculum. The goals of this Capacity Building project are to: 1) develop evidence-based innovative models and strategies for recruiting, preparing, and supporting teachers; 2) create plans for collecting data to determine need, interest, and capacity for increasing STEM teacher development; and 3) establish the infrastructure for preparing a Track 1: Scholarship & Stipend proposal in the future. This Capacity Building project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the persistence, retention, and effectiveness of K-12 STEM teachers in high-need school districts.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": 5, "pages": 1442, "count": 14420 } } }