Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1392&sort=-end_date
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-end_date", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-end_date", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1393&sort=-end_date", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1391&sort=-end_date" }, "data": [ { "type": "Grant", "id": "14510", "attributes": { "award_id": "2143449", "title": "CAREER: Why Are Ponds Biogeochemical Hotspots? Examining How Ecosystem Structure and Function Scale with Waterbody Size", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Cross-BIO Activities" ], "program_reference_codes": [], "program_officials": [ { "id": 29202, "first_name": "Kirsten", "last_name": "Schwarz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": null, "award_amount": 850000, "principal_investigator": { "id": 31149, "first_name": "Meredith", "last_name": "Holgerson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 279, "ror": "https://ror.org/05bnh6r87", "name": "Cornell University", "address": "", "city": "", "state": "NY", "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/>Ponds are small, shallow, and globally abundant waterbodies that provide important ecosystem services: ponds cycle nutrients, filter contaminants, promote high biodiversity, and regulate carbon emissions and storage. Several metrics of ecosystem function are disproportionately greater in ponds than lakes, including greenhouse gas emissions, ecosystem productivity, and nutrient concentrations. The greater biogeochemical activity in ponds may be due to stronger connections between bottom (benthic) and surface (pelagic) waters, a term called benthic-pelagic coupling. This research examines how benthic-pelagic coupling influences pond ecosystems using field surveys and whole-pond manipulations, which includes training of undergraduates, graduate students, and a postdoctoral researcher. This project further examines how the public use ponds and perceive water quality, informing the establishment of appropriate pond water quality standards that account for benthic-pelagic coupling. A key feature of this project is the close interaction of researchers with public agencies that regulate these aquatic habitats for citizen enjoyment and ecological functions.<br/><br/>Aquatic ecosystem function reflects processes occurring in both benthic and pelagic habitats, yet a framework integrating benthic-pelagic coupling with ecosystem function is lacking. This CAREER award links benthic-pelagic coupling to ecosystem function in temperate ponds, which offer an ideal study system due to the variable strength of benthic-pelagic coupling across ponds and their tractability for whole-ecosystem manipulations. The project focuses on two metrics of ecosystem function: ecosystem metabolism and greenhouse gas production and emissions, which reflect organic matter processing. The first goal of the project links the strength and timing of benthic-pelagic coupling to ecosystem function by sampling ponds with different mixing regimes, and by establishing experimental ponds that mix rarely, intermittently, or often. The second goal of the project determines how light availability and animal communities mediate benthic-pelagic coupling, which will be tested in experimental ponds and integrates research with an undergraduate limnology course. The third goal of the project establishes how benthic-pelagic coupling influences water chemistry and public perceptions of water quality, which includes public outreach, an undergraduate course, and partnerships with state waterbody managers. Ultimately, our framework to link benthic-pelagic coupling with pond carbon and nutrient cycling will help us to understand how small and shallow waterbodies function and predict how they will respond to environmental change such as warming, browning, eutrophication, and algal blooms.<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": "14511", "attributes": { "award_id": "2144766", "title": "CAREER: Large-scale Dynamic Reconfigurable Networks", "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": 1245, "first_name": "Deepankar", "last_name": "Medhi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": null, "award_amount": 570000, "principal_investigator": { "id": 2787, "first_name": "Manya", "last_name": "Ghobadi", "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": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).<br/><br/>Due to society’s ever-increasing dependency on online services, reliable working of the underlying communication networks is of paramount importance. For modern online services, emerging workloads (such as remote video calls, augmented reality, machine learning, and health care) depend highly on the underlying network’s response. However, the design of today’s network infrastructures still treats the physical layer of networks as a static black box with minimal reconfigurability. This project seeks to develop new paradigms for large-scale dynamic reconfigurable networks that are applicable to datacenter networks and software-defined private wide area networks to improve service delivery. The core mission of the project is to make physical layer reconfigurability an intrinsic part of future networks. <br/><br/>The project focuses on high-impact use-cases and applications to develop novel solutions for reconfigurable networks by leveraging optical technologies. To make large-scale reconfigurable networks a reality, this proposal tackles the foundational challenges of high-performance reconfigurable systems, including: (1) A set of algorithmic and system design techniques to co-optimize the network topology jointly with the parallelization strategy of emerging distributed machine learning jobs in datacenter networks. (2) A set of optimization and learning-based techniques to build practical cross-layer solutions for reconfigurable software-defined private wide-area networks while providing guaranteed performance. (3) Techniques to balance algorithmic and engineering foundations for reconfigurable systems. <br/><br/>Deploying reconfigurable networks will enable users around the world to have access to reliable and fast online services. As a result, this project has the potential of high industry impact. From an educational perspective, the project will develop a new graduate-level course on Systems for Machine Learning and Machine Learning for Systems. This emerging area at the intersection of machine learning and optical systems is driven by the explosive growth of diverse applications of artificial intelligence and the complexity of large-scale systems. This project will develop a variety of simulated and emulated environments with a focus on machine learning workloads and techniques which will be accessible to a large community of students and researchers who may not have expertise in these areas.<br/><br/>The data generated through the work in this project will consist of papers, source code, and benchmarks and will be released at the following website: http://reconfignets.csail.mit.edu/ Data will be retained for at least three years beyond the award period.<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": "14512", "attributes": { "award_id": "2144410", "title": "CAREER: Make Them Pay! Algorithms for Securing Wireless Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Information Technology Researc" ], "program_reference_codes": [], "program_officials": [ { "id": 27342, "first_name": "Peter", "last_name": "Brass", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": null, "award_amount": 404492, "principal_investigator": { "id": 31150, "first_name": "Maxwell", "last_name": "Young", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 716, "ror": "https://ror.org/0432jq872", "name": "Mississippi State University", "address": "", "city": "", "state": "MS", "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/>Wireless systems are often populated by energy-constrained devices, and communication occurs over a shared channel. These features make such systems especially vulnerable to jamming, whereby an adversary disrupts the shared channel, forcing devices to expend significant energy in order to mitigate the attack. The goal of this research is to design and analyze novel defenses that are energy-efficient, while still providing performance guarantees against a powerful jamming adversary. The outcomes of this research have the potential to secure systems against these attacks, whose severity is likely to escalate with the continued adoption of wireless technology. This project integrates its objectives with curriculum development and research opportunities for both undergraduate and graduate students at Mississippi State University. <br/><br/>The approach taken by this project differs from much of the prior work on jamming mitigation, which has focused solely on the energy cost incurred by correct devices. In practice, adversarial devices must also expend energy to jam the channel, and the aim of this research is to design defenses that exploit this aspect. Specifically, this project aims to develop algorithms that 1) guarantee correct devices can accomplish computational tasks despite jamming, 2) are energy-efficient in the absence of attack, and 3) impose an asymptotically-higher cost on the adversary relative to the correct devices when an attack is underway; such algorithms are called resource-competitive. Broadly, this project focuses on communication and coordination tasks that serve as fundamental building blocks for many wireless protocols. By providing a resource-competitive treatment of these tasks, this research addresses theoretical problems that have the potential to improve the security for a range of applications<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": "14513", "attributes": { "award_id": "2143021", "title": "CAREER: Transforming Distribution System Situational Awareness via Continuous-Time Adaptive Data Fusion", "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": 27281, "first_name": "Anthony", "last_name": "Kuh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": null, "award_amount": 500000, "principal_investigator": { "id": 31131, "first_name": "Yuzhang", "last_name": "Lin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 331, "ror": "https://ror.org/03hamhx47", "name": "University of Massachusetts Lowell", "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).<br/><br/>In the path to the clean energy future of the nation, the electric power industry is witnessing a massive shift from centralized fossil fuel generation to distributed renewable energy generation. Power distribution systems are responsible for connecting customers to the main grid and balancing the instantaneous generation-load mismatch at each customer. As power flows in the distribution systems become highly volatile and bidirectional, it is of crucial importance to gain situational awareness in real time such that grid operators can assess and enhance the renewable energy hosting capacity, and customers can reliably, resiliently, and efficiently buy/sell electricity to meet their needs. As a variety of data sources are being populated in distribution systems, the fundamental question remains how to extract and integrate the information to construct a complete picture of distribution system operation. Existing methods have not fully considered the complicated measurement environment pertaining to power distribution systems, and cannot produce accurate and reliable results when the measurements have diverse sampling rates, sampling times, accuracy classes, and with limited communication support. This project will develop transformative concepts and methodologies to comprehensively address the outstanding challenges in tracking the operating states of distribution systems. The outcomes of the project will fully bring out the potential of various sensor assets for distribution system situational awareness, which will serve as the foundation of intelligent decision making processes for accommodating the volatile but pervasive distributed renewable energy generation across the grid. The project will feature a Seeable Electrical Energy Distribution (SEED) program to integrate research with education. It will develop a simulation and visualization platform for a close-to-real synthetic distribution system “operating” in real time 24/7, providing educational experience to the wide public that has not been possible without entering control rooms of a utility company. The platform will also serve as a public data portal for researchers around the nation, facilitating data availability and research reproducibility across the whole technical community.<br/><br/>State estimation is a key technology for enabling the situational awareness of distribution systems and massive integration of distributed renewable energy generation. The existing distribution system state estimation methods largely inherit mature concepts from state estimation of high-voltage transmission systems, and do not fully consider or address the unique complicated measurement environment in distribution systems, including the unknown continuous-time state transition model, asynchronous and multi-rate measurements, unknown and time-varying measurement error statistics, and limited sampling rates and communication bandwidth. This project will propose a revolutionary distribution system state estimation paradigm that will transform the situational awareness of distribution systems for accommodating massive and pervasive renewable energy integration and demand response. We will develop new concepts and methodologies that result in a holistic solution allowing 1) learning-based continuous-time system dynamics modeling, 2) seamless fusion of asynchronous and multi-rate measurements arriving at any continuous time instants, 3) adaptive near-optimal estimation under unknown and time-varying measurement error statistics, and 4) proactive scheduling of sensor sampling times to maximize observability and minimize communication congestion using clustering. With the continuous-time data fusion feature, the proposed paradigm will replace the conventional discrete-time step-by-step estimation paradigm and reshape the field of distribution system state estimation. In a unique Seeable Electrical Energy Distribution (SEED) program, generative adversarial network will be exploited to synthesize distributed renewable energy and load data, which cannot be distinguished from real-world data yet do not have proprietary issues and can be freely distributed and reused by the research community.<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": "14514", "attributes": { "award_id": "2149956", "title": "REU Site: Interdisciplinary Computational Biology (iCompBio)", "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": 29245, "first_name": "Andrea", "last_name": "Holgado", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": null, "award_amount": 401980, "principal_investigator": { "id": 8018, "first_name": "Hong", "last_name": "Qin", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 894, "ror": "", "name": "University of Tennessee Chattanooga", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 8017, "first_name": "Yingfeng", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 894, "ror": "", "name": "University of Tennessee Chattanooga", "address": "", "city": "", "state": "TN", "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/>This REU Site award to the University of Tennessee at Chattanooga (UTC), located in Chattanooga, TN, will support the training of 10 students for 10 weeks during the summers of 2022- 2024. It is anticipated that a total of 30 students, primarily from schools with limited research opportunities or from a group under-represented in science, will be trained in the program. Students will learn to conduct multi-disciplinary computational research in the fields of cell biology, epidemiology, biodiversity, protein structure, biomechanics, ecology, bacterial membranes, climate, environmental sustainability, or others. Assessment of the program will be done through the online SALG URSSA tool. Students will be tracked after the program to determine their career paths. <br/><br/>Students participating in iCompBio will receive training in multidisciplinary research. Students will engage in mentored research experiences in computational biology across multiple research fields. In addition to short bootcamps and workshops on classical computational training such as bioinformatics, high-performance computing, mathematical modeling, and statistical analysis, iCompBio will provide research training in the application of machine-learning/deep learning, and quantum computing in computational biology, as well as research ethics and the responsible conduct of research. Research projects will take place in host labs under the guidance of research mentors. Students will meet weekly for joint computing workshops, research project troubleshooting, and to share their experiences and lessons learned. Informal lunch meetings will be held to discuss topics such as career choices, equity in research, and work-life balance with mentors, graduate students, or guests. Multiple group outings to local and regional attractions will be taken. Two research seminars will be held with other UTC-based summer research programs. Students interested in the program should complete an online application form, and submit unofficial transcripts, personal statement, resume/CV, and two letters of recommendation. Applications will be reviewed by a team of faculty. More information about the program is available by visiting http://utc.edu/icompbio or by contacting the PI (Dr. Hong Qin at [email protected] ) or the co-PI (Dr. Yingfeng Wang at [email protected]).<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": "14515", "attributes": { "award_id": "2046240", "title": "CAREER: Coastal Antarctic Snow Algae and Light Absorbing Particles: Snowmelt, Climate and Ecosystem Impacts", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "ANT Organisms & Ecosystems" ], "program_reference_codes": [], "program_officials": [ { "id": 13985, "first_name": "Rebecca", "last_name": "Gast", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 362, "ror": "https://ror.org/03zbnzt98", "name": "Woods Hole Oceanographic Institution", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] } ], "start_date": "2022-01-01", "end_date": null, "award_amount": 1228430, "principal_investigator": { "id": 31151, "first_name": "Alia", "last_name": "Khan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 481, "ror": "https://ror.org/05wn7r715", "name": "Western Washington University", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "________________________________________________________________________________________________<br/><br/><br/><br/><br/>Part I: Non-technical Summary<br/>The Antarctic Peninsula is one of the most rapidly warming regions on the planet. This 5-yr time-series program will build on an ongoing international collaboration with scientists from the Chilean Antarctic Program to evaluate the role of temperature, light absorbing particles, snow-algae growth, and their radiative forcing effects on snow and ice melt in the Western Antarctic Peninsula. There is strong evidence that these effects may be intensifying due to a warming climate. Rising temperatures can increase the growth rate of coastal snow algae as well as enhance the input of particles from sources such as the long-range transport of black carbon to the Antarctic continent from intensifying Southern Hemisphere wildfire seasons. Particle and algae feedbacks can have immediate local impacts on snow melt and long-term regional impacts on climate because reduced snow cover alters how the Antarctic continent interacts with the rest of the global climate. A variety of ground-based and remote sensing data collected across multiple spatial scales will be used. Ground measurements will be compared to satellite imagery to develop novel computer algorithms to map ice algal bloom effects under changing climates. The project is expected to fundamentally advance knowledge of the spatial and temporal snow algae growing season, which is needed to quantify impacts on regional snow and ice melt. The program also has a strong partnership with the International Association of Antarctic Tour Operators to involve cruise passengers as citizen scientists for sample collection. Antarctic research results will be integrated into undergraduate curricula and research opportunities through studies to LAPs and snow algae in the Pacific Northwest. The PI will recruit and train a diverse pool of students in cryosphere climate related research methods on Mt. Baker in Western Washington. Trained undergraduate will then serve as instructors for a local Snow School that takes middle school students to Mt. Baker to learn about snow science. Resulting datasets from Antarctica and Mt. Baker will be used in University classes to explore regional effects of climate change. Along with enhancing cryosphere-oriented place-based undergraduate field courses in the Pacific Northwest, the PI will recruit and train a diverse pool of undergraduate students to serve as instructors for the Mt. Baker Snow School program. This award will advance our understanding of cryosphere-climate feedbacks, which are likely changing and will continue to evolve in a warming world, while also increasing under-represented student engagement in the polar geosciences. <br/><br/>Part 2: Technical Summary <br/>Rapid and persistent climate warming in the Western Antarctic Peninsula is likely resulting in intensified snow-algae growth and an extended bloom season in coastal areas. Similarly, deposition of light absorbing particles (LAPs) onto Antarctica cryosphere surfaces, such as black carbon from intensifying Southern Hemisphere wildfire seasons, and dust from the expansion of ice-free regions in the Antarctic Peninsula, may be increasing. The presence of snow algae blooms and LAPs enhance the absorption of solar radiation by snow and ice surfaces. This positive feedback creates a measurable radiative forcing, which can have immediate local and long-term regional impacts on albedo, snow melt and downstream ecosystems. This project will investigate the spatial and temporal distribution of snow algae, black carbon and dust across the Western Antarctica Peninsula region, their response to climate warming, and their role in regional snow and ice melt. Data will be collected across multiple spatial scales from in situ field measurements and sample collection to imagery from ground-based photos and high resolution multi-spectral satellite sensors. Ground measurements will inform development and application of novel algorithms to map algal bloom extent through time using 0.5-3m spatial resolution multi-spectral satellite imagery. Results will be used to improve snow algae parameterization in a new version of the Snow Ice Aerosol Radiation model (SNICARv3) that includes bio-albedo feedbacks, eventually informing models of ice-free area expansion through incorporation of SNICARv3 in the Community Earth System Model. Citizen scientists will be mentored and engaged in the research through an active partnership with the International Association of Antarctic Tour Operators that frequently visits the region. The cruise ship association will facilitate sampling to develop a unique snow algae observing network to validate remote sensing algorithms that map snow algae with high-resolution multi-spectral satellite imagery from space. These time-series will inform instantaneous and interannual radiative forcing calculations to assess impacts of snow algae and LAPs on regional snow melt. Quantifying the spatio-temporal growing season of snow algae and impacts from black carbon and dust will increase our ability to model their impact on snow melt, regional climate warming and ice-free expansion in the Antarctic Peninsula region.<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": "14516", "attributes": { "award_id": "2050372", "title": "REU Site: Collaborative Research: Research Opportunities in Rock Deformation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Petrology and Geochemistry" ], "program_reference_codes": [], "program_officials": [ { "id": 9801, "first_name": "Aisha", "last_name": "Morris", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-01", "end_date": null, "award_amount": 359067, "principal_investigator": { "id": 31152, "first_name": "Philip", "last_name": "Skemer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 304, "ror": "", "name": "Washington University", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "Rock deformation, a sub-discipline of Earth science, uses geology and engineering methods to measure the strength of rocks. Information about rock strength can be applied to a wide range of problems in engineering, natural hazards, and material science, as well as geology. However, very few scientists specialize in the field of rock deformation; thus, many important research questions remain unexplored or underexplored, slowing advances in science and engineering. Geoscience departments at primarily undergraduate or primarily minority-serving institutions often do not have active rock deformation research programs and labs that deal with rock deformation are relatively rare. This REU site aims to increase access and exposure of a diverse population of students to career opportunities in this area. The project offers undergraduate students the unique opportunity to conduct summer research at one of eleven state-of-the-art, experimental rock deformation labs across the country. Experts in rock deformation will serve as mentors to a cohort of 10 students each summer. This REU site provides access and training, professional and career development activities, and robust workforce development for students from all backgrounds.<br/><br/>The REU site will provide research and mentorship opportunities for undergraduate students in the field of experimental rock deformation. The long-term objective is to increase the number and diversity of students pursuing research or industry careers in rock deformation. Student participants will receive training in research methods and professional development topics that will provide a stable foundation for graduate school or related career paths. A large team of PIs and senior participants ensures that students who participate in the program will have a deep professional network to support their future endeavors. Students will be drawn from the full spectrum of higher education institutions. Strong emphases will be placed on recruiting students from diverse and under-represented backgrounds, and smaller colleges and universities that do not have research programs in rock deformation. The REU site will include three integrated sessions: a field session to introduce students to the geological study of deformed rocks, a laboratory session where students conduct experiments on specimens collected during the field session, and a conference session where students can present the results of their research projects. The REU site will adopt a distributed model, leveraging the combined lab capacity of the PIs and other senior participants to support 10 students per year.<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": "14517", "attributes": { "award_id": "2114051", "title": "Arctic Beaver Observation Network (A-BON): Tracking a new disturbance regime", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "NNA-Navigating the New Arctic" ], "program_reference_codes": [], "program_officials": [ { "id": 15971, "first_name": "Roberto", "last_name": "Delgado", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": null, "award_amount": 2509381, "principal_investigator": { "id": 31154, "first_name": "Kenneth", "last_name": "Tape", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 29450, "first_name": "Benjamin M", "last_name": "Jones", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31153, "first_name": "Caroline L", "last_name": "Brown", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 435, "ror": "", "name": "University of Alaska Fairbanks Campus", "address": "", "city": "", "state": "AK", "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/>Beavers are remarkable animals that engineer the landscape by building dams and transforming linear streams into a series of ponds. The impact of this engineering on lowland ecosystems is widely recognized in temperate regions, but beavers are now moving from the forest into arctic tundra regions, where their extent and impacts are unknown. In the Arctic, perennially frozen ground – permafrost – stores vast amounts of carbon, and the stability of permafrost is jeopardized when the landscape is inundated due to beaver engineering and changes in hydrology. Furthermore, stream life in the Arctic is limited by low temperatures, but deeper water associated with pond building increases water temperature in winter. This in turn alters stream ecology and may create refugia for new species moving into the Arctic, including fish. Beaver expansion into new areas has been observed with concern by people living in affected regions, particularly potential impacts to fish, water quality, and boat access. This research will co-produce knowledge with local people to understand concerns and observed impacts of beaver colonization. Scientific fieldwork at beaver ponds will illuminate changes in hydrology and permafrost. This understanding will be combined with satellite images of beaver ponds across the entire Arctic to determine the extent of beaver engineering and recent changes. This research is vital to our understanding of the current and future arctic ecosystem; it will be shared widely through education and media, including a Netflix series featuring beavers moving into tundra.<br/><br/>The Arctic Beaver Observation Network (A-BON) will observe beaver engineering across circumarctic treeline and tundra environments during the last half-century by mapping and tracking beaver ponds using satellite imagery. Investigators will work closely with residents of three Alaskan communities (Shungnak, Kotzebue, and Noatak) to document long-term, experiential observations of beaver habitat and activity, particularly changes wrought to the landscape and to fish. These observations will provide local to regional insights clarifying the timing of beaver arrival and the impacts to socio-ecological systems. Scientific fieldwork will characterize how beavers alter physical attributes of the aquatic and terrestrial tundra environment, from initial perturbation to ongoing landscape evolution. A-BON will coordinate circumarctic efforts surrounding this issue, encouraging dialogue and data sharing among local communities, scientists, and land managers. A-BON adds to the NSF Arctic Observing Network portfolio a project with a strong remote sensing component complemented by a strong local need, perspective and collaboration. A-BON will provide a baseline circumarctic understanding of a new disturbance regime. This co-production of knowledge will inform a multitude of related studies on the ramifications of beaver colonization, from energy cycling to fish, riparian vegetation, subsistence land-use practices and human health.<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": "14518", "attributes": { "award_id": "2141095", "title": "EAGER: Social Media Based Early Cybersecurity Threat Detection", "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": 849, "first_name": "Dan", "last_name": "Cosley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": null, "award_amount": 100000, "principal_investigator": { "id": 31155, "first_name": "Chang-Tien", "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": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Society’s ever-increasing reliance on the complex digital world with a huge repository of highly confidential and private data has made maintaining cybersecurity an uncompromising task for organizations. The loss incurred by the US healthcare system due to ransomware attacks has exceeded 157 million dollars since 2016. Traditionally, cyberattack detection techniques leverage network traffic data to detect certain types of attacks. However, this kind of approach is difficult to generalize. Furthermore, the requisite data is too expensive to obtain and information about organizational compromise can often originate outside the institution. Hence, open source indicators like social media platforms, which propagate rich security-related discussions for ongoing cyberattacks, can be inexpensive yet effective sources of data for an early cyberattack detection system. This project aims to implement a social media based multitask active learning framework for early cybersecurity threat detection. Its reliance on open source data will generalize the application of the research across different target entities. The project will produce a theoretical framework for teaching cyberattack detection or social media mining, providing academia and the industry a broader understanding of fundamental methodological approaches.<br/><br/>This research will design the solution through different interconnected research thrusts. The key challenges in social media based cyberattack detection are lack of comprehensive ground truth data and expensive labeling effort. The project tackles this problem by innovatively incorporating both dynamic query expansion and active learning. The dynamic query expansion component provides an effective procedure to collect domain specific labeled data while the active learning module interactively updates the training dataset by labeling the data collected outside the constraints of the dynamic query expansion. Furthermore, to address the problem of generalizing over various types of cyberattacks, the project explores a novel multitask learning framework with message passing mechanism to model varied and distinctive types of cybersecurity events. Additionally, the investigator intends to implement a visual interface which explores novel deep learning-based storyline generation techniques for the detected security events which will provide an interpretable visual analysis of cybersecurity related incidents for different organizations across time.<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": "14519", "attributes": { "award_id": "2141095", "title": "EAGER: Social Media Based Early Cybersecurity Threat Detection", "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": 849, "first_name": "Dan", "last_name": "Cosley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": null, "award_amount": 100000, "principal_investigator": { "id": 31155, "first_name": "Chang-Tien", "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": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Society’s ever-increasing reliance on the complex digital world with a huge repository of highly confidential and private data has made maintaining cybersecurity an uncompromising task for organizations. The loss incurred by the US healthcare system due to ransomware attacks has exceeded 157 million dollars since 2016. Traditionally, cyberattack detection techniques leverage network traffic data to detect certain types of attacks. However, this kind of approach is difficult to generalize. Furthermore, the requisite data is too expensive to obtain and information about organizational compromise can often originate outside the institution. Hence, open source indicators like social media platforms, which propagate rich security-related discussions for ongoing cyberattacks, can be inexpensive yet effective sources of data for an early cyberattack detection system. This project aims to implement a social media based multitask active learning framework for early cybersecurity threat detection. Its reliance on open source data will generalize the application of the research across different target entities. The project will produce a theoretical framework for teaching cyberattack detection or social media mining, providing academia and the industry a broader understanding of fundamental methodological approaches.<br/><br/>This research will design the solution through different interconnected research thrusts. The key challenges in social media based cyberattack detection are lack of comprehensive ground truth data and expensive labeling effort. The project tackles this problem by innovatively incorporating both dynamic query expansion and active learning. The dynamic query expansion component provides an effective procedure to collect domain specific labeled data while the active learning module interactively updates the training dataset by labeling the data collected outside the constraints of the dynamic query expansion. Furthermore, to address the problem of generalizing over various types of cyberattacks, the project explores a novel multitask learning framework with message passing mechanism to model varied and distinctive types of cybersecurity events. Additionally, the investigator intends to implement a visual interface which explores novel deep learning-based storyline generation techniques for the detected security events which will provide an interpretable visual analysis of cybersecurity related incidents for different organizations across time.<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 } } ], "meta": { "pagination": { "page": 1392, "pages": 1419, "count": 14184 } } }