Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1405&sort=-id
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-id", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-id", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1406&sort=-id", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1404&sort=-id" }, "data": [ { "type": "Grant", "id": "523", "attributes": { "award_id": "2014053", "title": "Collaborative Research: Segmentation of Time Series via Self-Normalization", "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": 1078, "first_name": "Huixia", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2023-06-30", "award_amount": 65677, "principal_investigator": { "id": 1079, "first_name": "Zifeng", "last_name": "Zhao", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to develop new statistical methodology and theory for change-point analysis of time series data. Change-point models have wide applications in many scientific areas, including modeling the daily volatility of the U.S. financial market, and the weekly growth rate of an infectious disease such as coronavirus, among others. Compared with existing methodologies, this research will provide inference for a flexible range of change point models, which will remain valid under complex dependence relationships exhibited by real datasets. The methodologies ensuing from the project will be disseminated to the relevant scientific communities via publications, conference and seminar presentations, and the development of open-source software. The Principal Investigators (PIs) will jointly mentor a Ph.D. student and involve undergraduate students in the research, and offer advanced topic courses to introduce the state-of-the-art techniques in time series analysis.Time series segmentation, also known as change-point estimation, is one of the fundamental problems in statistics, where a time series is partitioned into piecewise homogeneous segments such that each piece shares the same behavior. There is a vast body of literature devoted to change-point estimation in independent observations; however, robust methodology and rigorous theory that can accommodate temporal dependence is still scarce. Motivated by the recent success of the self-normalization (SN) method, which was developed by one of the PIs for structural break testing and other inference problems in time series, the PIs will advance the self-normalization technique to time series segmentation. Specifically, the PIs will develop a systematic and unified SN-based change-point estimation methodology and the associated theory for (i) segmenting a piecewise stationary time series into homogeneous pieces so within each piece a finite dimensional parameter is constant; (ii) segmenting a linear trend model with stationary and weakly dependent errors into periods with constant slope. The segmentation algorithms to be developed are broadly applicable to fixed-dimensional time series data and can be further extended to cover high-dimensional and locally stationary time series with proper modification of the self-normalized test statistics.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": "522", "attributes": { "award_id": "2014018", "title": "Collaborative Research: Segmentation of Time Series via Self-Normalization", "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": 1076, "first_name": "Huixia", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-15", "end_date": "2023-06-30", "award_amount": 149999, "principal_investigator": { "id": 1077, "first_name": "Xiaofeng", "last_name": "Shao", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to develop new statistical methodology and theory for change-point analysis of time series data. Change-point models have wide applications in many scientific areas, including modeling the daily volatility of the U.S. financial market, and the weekly growth rate of an infectious disease such as coronavirus, among others. Compared with existing methodologies, this research will provide inference for a flexible range of change point models, which will remain valid under complex dependence relationships exhibited by real datasets. The methodologies ensuing from the project will be disseminated to the relevant scientific communities via publications, conference and seminar presentations, and the development of open-source software. The Principal Investigators (PIs) will jointly mentor a Ph.D. student and involve undergraduate students in the research, and offer advanced topic courses to introduce the state-of-the-art techniques in time series analysis.Time series segmentation, also known as change-point estimation, is one of the fundamental problems in statistics, where a time series is partitioned into piecewise homogeneous segments such that each piece shares the same behavior. There is a vast body of literature devoted to change-point estimation in independent observations; however, robust methodology and rigorous theory that can accommodate temporal dependence is still scarce. Motivated by the recent success of the self-normalization (SN) method, which was developed by one of the PIs for structural break testing and other inference problems in time series, the PIs will advance the self-normalization technique to time series segmentation. Specifically, the PIs will develop a systematic and unified SN-based change-point estimation methodology and the associated theory for (i) segmenting a piecewise stationary time series into homogeneous pieces so within each piece a finite dimensional parameter is constant; (ii) segmenting a linear trend model with stationary and weakly dependent errors into periods with constant slope. The segmentation algorithms to be developed are broadly applicable to fixed-dimensional time series data and can be further extended to cover high-dimensional and locally stationary time series with proper modification of the self-normalized test statistics.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": "521", "attributes": { "award_id": "2016026", "title": "ACL 2020 Student Workshop", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1074, "first_name": "Tatiana", "last_name": "Korelsky", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2020-11-30", "award_amount": 14980, "principal_investigator": { "id": 1075, "first_name": "Zhou", "last_name": "Yu", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 276, "ror": "", "name": "University of California-Davis", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 276, "ror": "", "name": "University of California-Davis", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Association for Computational Linguistics (ACL) is the primary international organization for computational linguistics and natural language processing. This is one of the primary application areas for researchers in machine learning and artificial intelligence. The proceedings of the ACL's annual meeting provide the foundation of the field; it is the most cited and most respected publication in computational linguistics. Thus, it is also the most important gathering of researchers in computational linguistics and natural language processing, and represents one of the most important opportunities for young researchers to understand the field, build professional connections, and gain exposure. ACL has a twenty-one-year history of supporting these formative activities by holding a student research workshop. The workshops have proven popular: attracting dozens of submissions from highly qualified applicants.The student research workshop will be a part of the 2020 Meeting of the Association for Computational Linguistics (ACL), to be held in conjunction with the main conference July 5th –July 10th, 2020. This year ACL will be held online due to COVID-19 and the workshop will subsidize only conference registration for students who are selected to participate. The workshop will solicit submissions in two categories: (1) *thesis proposals* for advanced students who have decided on a thesis topic and wish to receive feedback and (2) *research papers* describing completed work or work in progress with significant preliminary results. Each accepted paper will be assigned an established research mentor who will meet with the student during the conference to provide individual feedback. Both paper types will be presented in the poster session of the main conference.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": "520", "attributes": { "award_id": "2011846", "title": "Bioinspired Soft Materials", "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": 1072, "first_name": "Miriam", "last_name": "Deutsch", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2026-08-31", "award_amount": 6000000, "principal_investigator": { "id": 1073, "first_name": "Seth", "last_name": "Fraden", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 280, "ror": "https://ror.org/05abbep66", "name": "Brandeis University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 280, "ror": "https://ror.org/05abbep66", "name": "Brandeis University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Nontechnical Abstract: The Brandeis Bioinspired Soft Materials Research Science and Engineering Center (MRSEC) seeks to engineer new materials that capture the remarkable functionalities found in living organisms. To realize this vision the Center synergistically focuses on two topics at the forefront of Soft Materials research. The first, Self-Limiting Assembly, adopts a bioinspired approach to develop a suite of building blocks which undergo equilibrium self-assembly that terminates at a prescribed finite-size. Learning to engineer self-limiting structures is enabling the scalable design of new functional and adaptable materials, such as paintable photonic coatings and capsids for drug therapy including general strategies to deactivate virus infection, including covid-19. The second, Soft Active Materials, is inspired by the remarkable capabilities of living cells, with their abilities to sense, self-heal and move. This MRSEC is establishing the principles to design, measure and control the forces in active materials to generate rapidly reconfigurable life-like materials, with applications in fields as diverse as robotics, microfluidics and adaptive optics. The MRSEC emphasizes human resource development for the STEM workforce. To facilitate this is the MRSEC’s SciComm Lab comprising science graduate students and postdocs trained to become effective communicators and peer mentors. These SciComm Lab mentors train their peers in the skills they need to communicate their science to disparate audiences ranging from future employers to a diverse, non-scientific public. The Path-to-Professorship Program, aimed at providing superior postdoctoral fellow training for underrepresented minorities in cutting-edge materials research and designed to prepare individuals for assuming full-time faculty positions.Technical Abstract: The Brandeis Bioinspired Soft Materials Research Science and Engineering Center (MRSEC)is engineering new materials that capture the remarkable functionalities found in living organisms. The Center is organized into two Interdisciplinary Research Groups (IRG). IRG1, Self-Limiting Assembly, addresses a grand challenge in soft materials science, the self-assembly of complex and functional materials. While living systems routinely achieve size-controlled assembly, synthetic approaches lag far behind. IRG1 adopts a bioinspired approach to develop a suite of building blocks which undergo equilibrium self-assembly that self-terminates at a prescribed finite-size. Learning to engineer self-limiting structures is enabling scalable design of new functional and adaptable materials, such as paintable photonic coatings and capsids for drug and gene therapy. IRG2, Soft Active Materials, is inspired by the remarkable capabilities of living cells, such as crawling, reconfiguring, and regenerating which are driven by energy-consuming molecular motors. An unmet grand challenge is to construct artificial active materials with active stresses designed to produce a desired function. This is leading to the next generation of active materials that are robust and exhibit long-lived programmable dynamics, thus paving the way to applications. This MRSEC emphasizes human resource development for the STEM workforce. To facilitate this is the MRSEC's SciComm Lab comprising science graduate students and postdocs trained to become effective communicators and peer mentors. The Path-to-Professorship Program, aimed at providing superior postdoctoral fellow training for underrepresented minorities in cutting-edge materials research and designed to prepare individuals for assuming full-time faculty positions.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": "519", "attributes": { "award_id": "2015379", "title": "Collaborative Research: Extremes in High Dimensions: Causality, Sparsity, Classification, Clustering, Learning", "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": 1069, "first_name": "Pena", "last_name": "Edsel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2023-06-30", "award_amount": 299999, "principal_investigator": { "id": 1071, "first_name": "Richard A", "last_name": "Davis", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1070, "first_name": "Marco Avella", "last_name": "Medina", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 196, "ror": "https://ror.org/00hj8s172", "name": "Columbia University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "In recent years, through news reports and first-hand experience, the general public has become keenly aware of extreme events, in particular, of extreme weather conditions such as extended heat waves, periods of extreme cold, an increase in the number and intensity of tornadoes and hurricanes, or periods of record precipitation resulting in unprecedented floods. Just in the past few years, the insurance claims from extreme climatic events have been staggering, which include the Missouri River flood in April 2019 ($10.8B), Hurricane Michael in October 2018 ($25B), the California wildfires in December 2017 ($18.7B), the US drought/heatwave in 2012 ($33.9B), and Hurricane Sandy in October 2012 ($73.4B). This list does not include non-climatic extreme events such as the financial crisis from 2008 nor the current covid-19 pandemic. Many of the extreme events experienced today that are weather, environmental, industrial, epidemiological, economic, or social media related are occurring at a more frequent rate, which often result in huge losses to our society in a variety of ways from financial to human life to our way of life. While the occurrence of extreme events is reasonably well understood in steady state situations, it has become clear that the preponderance of extremes events suggest that the steady-state assumption is no longer valid. The key objective of this research is to try to understand causal impacts of various factors from a potentially large array of variables including changing environmental conditions, demographic movements within the US, changing landscapes, and changing economic conditions, on the frequency and magnitude of extreme events. From many variables, we hope to produce methodology to extract the important features in the data that have a direct impact on describing and predicting extremes. This research is potentially of use to policymakers who need to anticipate and plan for extreme events leading to sensible strategies for mitigating their impact on society. The graduate student support will be used for interdisciplinary research.The principal goal of this research project is to design new tools for analyzing and modeling extremes in a myriad of situations that go well beyond the boundaries of classical extreme value theory. These include detection of often nonlinear sets of much smaller dimension that can provide an adequate description of extremes in high dimensions, for which we hope to apply the powerful modern learning techniques (such as graph-based learning methods) that allow us to determine this extremal support from the data. In general, detecting sparsity in the exponent measure describing high-dimensional extremes, i.e., locating (often numerous) low-dimensional regions which carry most of the support of exponent measure will be a key focus of this research. A second main thrust of this research centers on the issue of causality in both small and large dimensional problems. In the most basic form, a set of variables X is said to be tail causal to a dependent vector Y if certain changes in X (sometimes themselves extreme but not always so) impact the tail behavior of Y. An important setting of this type is the potential outcomes framework for causality of extreme events, which will be a major focus in this project's research agenda.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": "518", "attributes": { "award_id": "2034656", "title": "MsRI-EW: Conference to Identify Research Infrastructure Concepts for a National Full-Scale 200 mph Wind and Wind-Water Testing Facility; Virtual; August 2020", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1065, "first_name": "Joy", "last_name": "Pauschke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2021-06-30", "award_amount": 49624, "principal_investigator": { "id": 1068, "first_name": "Arindam", "last_name": "Chowdhury", "orcid": null, "emails": "[email protected]", "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": 1066, "first_name": "Ioannis G", "last_name": "Zisis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1067, "first_name": "Amal", "last_name": "Elawady", "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": "To foster national resilience, the United States needs advanced experimental capabilities that can support research in understanding and reducing human, infrastructure, and property losses from extreme wind and wind-water events, particularly hurricanes. Recent windstorms have demonstrated rapid intensification where wind gusts have exceeded 200 miles per hour (mph), and there are indications that the number of very high intensity windstorms will increase in the coming years. To mitigate the impact of extreme wind and wind-water events on the built environment, a university-based research and testing facility is needed that can simulate wind fields reaching 200 mph. This award will support an engineering workshop to identify research infrastructure concepts for a national, full-scale, 200 mph wind and wind-water testing facility capable of supporting research and testing beyond the wind speeds and scales that are achievable with current testing facilities in the United States. Additionally, because storm surge (wind-driven water) is the principal life safety threat in hurricanes, the workshop will identify ways to integrate storm surge and wave actions into the facility design. The long-term goal of the workshop is to advance wind engineering research with new testing capabilities that can enhance the hurricane resilience of the built environment. This workshop supports the National Science Foundation's role in the National Windstorm Impact Reduction Program. The workshop report will be disseminated to the natural hazards and engineering research communities via the Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.DesignSafe-ci.org). The two-day workshop will be held virtually in August 2020 due to the ongoing COVID-19 pandemic. The workshop will identify conceptual gaps in existing mid-scale research infrastructure (MsRI) and assess the feasibility and design options for a national, full-scale, 200 mph wind and wind-water testing facility. The workshop, hosted by Florida International University, will include 50 participants from research and engineering applications communities from universities, businesses, professional associations, and government. The workshop will include design and construction conceptualization of a unique MsRI to (a) advance knowledge on the characterization of the transient nature of wind-surge-wave combined hazards, and (b) enable robust simulations of the behavior of civil infrastructure under the multi-stressor environment of hurricane landfalls. The workshop will include a project management expert and a conference facilitator to work with plenary leaders and breakout session chairs. The outcome of the workshop will be a report that will identify the research needs and a conceptual design for the facility.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": "517", "attributes": { "award_id": "2015242", "title": "Collaborative Research: Extremes in High Dimensions: Causality, Sparsity, Classification, Clustering, Learning", "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": 1063, "first_name": "Pena", "last_name": "Edsel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2023-06-30", "award_amount": 300000, "principal_investigator": { "id": 1064, "first_name": "Gennady", "last_name": "Samorodnitsky", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 279, "ror": "https://ror.org/05bnh6r87", "name": "Cornell University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "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": "In recent years, through news reports and first-hand experience, the general public has become keenly aware of extreme events, in particular, of extreme weather conditions such as extended heat waves, periods of extreme cold, an increase in the number and intensity of tornadoes and hurricanes, or periods of record precipitation resulting in unprecedented floods. Just in the past few years, the insurance claims from extreme climatic events have been staggering, which include the Missouri River flood in April 2019 ($10.8B), Hurricane Michael in October 2018 ($25B), the California wildfires in December 2017 ($18.7B), the US drought/heatwave in 2012 ($33.9B), and Hurricane Sandy in October 2012 ($73.4B). This list does not include non-climatic extreme events such as the financial crisis from 2008 nor the current covid-19 pandemic. Many of the extreme events experienced today that are weather, environmental, industrial, epidemiological, economic, or social media related are occurring at a more frequent rate, which often result in huge losses to our society in a variety of ways from financial to human life to our way of life. While the occurrence of extreme events is reasonably well understood in steady state situations, it has become clear that the preponderance of extremes events suggest that the steady-state assumption is no longer valid. The key objective of this research is to try to understand causal impacts of various factors from a potentially large array of variables including changing environmental conditions, demographic movements within the US, changing landscapes, and changing economic conditions, on the frequency and magnitude of extreme events. From many variables, we hope to produce methodology to extract the important features in the data that have a direct impact on describing and predicting extremes. This research is potentially of use to policymakers who need to anticipate and plan for extreme events leading to sensible strategies for mitigating their impact on society. The graduate student support will be used for interdisciplinary research.The principal goal of this research project is to design new tools for analyzing and modeling extremes in a myriad of situations that go well beyond the boundaries of classical extreme value theory. These include detection of often nonlinear sets of much smaller dimension that can provide an adequate description of extremes in high dimensions, for which we hope to apply the powerful modern learning techniques (such as graph-based learning methods) that allow us to determine this extremal support from the data. In general, detecting sparsity in the exponent measure describing high-dimensional extremes, i.e., locating (often numerous) low-dimensional regions which carry most of the support of exponent measure will be a key focus of this research. A second main thrust of this research centers on the issue of causality in both small and large dimensional problems. In the most basic form, a set of variables X is said to be tail causal to a dependent vector Y if certain changes in X (sometimes themselves extreme but not always so) impact the tail behavior of Y. An important setting of this type is the potential outcomes framework for causality of extreme events, which will be a major focus in this project's research agenda.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": "516", "attributes": { "award_id": "2003587", "title": "Electrochemical Methodology for Single Molecule Enzymology", "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": 1061, "first_name": "Robin", "last_name": "McCarley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2023-06-30", "award_amount": 462508, "principal_investigator": { "id": 1062, "first_name": "Jeffrey E", "last_name": "Dick", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 166, "ror": "https://ror.org/0130frc33", "name": "University of North Carolina at Chapel Hill", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 166, "ror": "https://ror.org/0130frc33", "name": "University of North Carolina at Chapel Hill", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Enzymes are special proteins inside cells that hold chemical reactants in precise positions to accelerate bio-chemical reactions. While they are involved in chemical reactions, enzymes are not used up in the reactions. Studying these reactions at a fundamental level is particularly challenging, because it requires detecting individual enzymes. With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Jeffrey E. Dick at the University of North Carolina at Chapel Hill is advancing electrochemical methods for studying single enzymes. Working with his students, Professor Dick is creating tiny electrodes that can characterize chemical reactions catalyzed by a single enzyme in volumes smaller than a single cell. The project has the potential to offer new ways of studying chemical reactions in cells and impact the development of ultrasensitive biosensors. In addition, the activities provide training opportunities for graduate and undergraduate students who will become the next generation of electrochemists and biotechnologists. The group is also developing electrochemical instruments for use in high schools around the United States, broadening the impact of the project beyond the research laboratory.With this award, the Chemical Measurement and Imaging Program is funding Dr. Jeffrey E. Dick at the University of North Carolina at Chapel Hill to develop electrochemical methods to detect and quantify single enzyme molecules. The amount of current a single enzyme produces depends on its maximum turnover rate. Common enzyme turnover rates are on the order of 1000 Hz, implying the amount of current generated by a single enzyme is under a femtoampere. This tiny current cannot be detected and quantified with reasonable bandwidth. With his students, Dr. Dick is developing potentiometric-based methods to address this fundamental limitation. Potentiometry is a powerful technique in that it requires a very small, variable bias current to produce a potential measurement, and the measurement itself is independent of electrode size. The amplification of a single enzyme’s turnover is achieved by trapping a single enzyme molecule within a sub-attoliter volume, synthesized as an emulsion or fabricated via nanofabrication. In these small volumes, effectively a single enzyme biosensor, a single enzyme’s physicochemical properties are rigorously evaluated using novel electrochemistry-based measurement methods developed in this research project. The project also focuses on methods that hold promise to specifically and rapidly detect single virus particles such as COVID-19.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": "515", "attributes": { "award_id": "2028419", "title": "Travel Support: Student Program for Practice and Experience in Advanced Research Computing Conference (PEARC20)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1057, "first_name": "Alan", "last_name": "Sussman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2022-06-30", "award_amount": 10000, "principal_investigator": { "id": 1060, "first_name": "Elizabett A", "last_name": "Hillery", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1058, "first_name": "Gwen A", "last_name": "Jacobs", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1059, "first_name": "Preston M", "last_name": "Smith", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "The goal of the Practice and Experience in Advanced Research Computing (PEARC) Conference series is to provide a forum for discussing challenges, opportunities, and solutions among High Performance Computing (HPC) center directors and managers, computational scientists, end users, students, facilitators, educators, HPC system administrators and user support staff, as well as industry and government agency representatives from across the United States and around the world. The conference follows the successful five-year conference series that was hosted by the eXtreme Science and Engineering Discovery Environment (XSEDE) program. Building on the success of XSEDE conference series, PEARC aims to broaden the community by including additional campus, national, and international cyberinfrastructure and research computing partners. The conference will be held online from July 26-30, 2020. The project will fund students to participate in the conference, especially in the student program activities, and also fund a small number of junior faculty and researchers to participate in the conference. Some students are presenting papers and posters at the conference, will be given priority for funding The project serves the national interest, as stated by NSF's mission, to promote the progress of science as it provides a forum to disseminate research efforts, connect researchers, and train the next generation of scholars.Due to Covid-19 travel restrictions, the conference will be fully virtual. The student program committee for the conference is recruiting students with an emphasis on diversity and inclusion of underrepresented groups and from a diverse set of institutions. For the student program, students will participate in (1) a student mentor program, which pairs a student with a senior mentor to help guide the student through conference activities, (2) a student volunteer program, to help run and organize the virtual conference, (3), a speed networking session, where students can meet 1-on-1 with conference exhibitors, and (4) a panel discussion, where students will attend a panel conversation about careers in HPC and career paths into HPC. The funding provided by NSF will have a significant impact on the careers of the future generation of researchers in high performance computing, while encouraging diversity in the field.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": "514", "attributes": { "award_id": "2007638", "title": "School on Electron-Phonon Physics from First Principles", "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": 1053, "first_name": "Daryl", "last_name": "Hess", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2022-07-31", "award_amount": 117867, "principal_investigator": { "id": 1056, "first_name": "Feliciano", "last_name": "Giustino", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1054, "first_name": "Emmanouil", "last_name": "Kioupakis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1055, "first_name": "Elena Roxana", "last_name": "Margine", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 156, "ror": "", "name": "University of Texas at Austin", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "NONTECHNICAL SUMMARYThis award supports a summer school activity to train graduate students, postdoctoral scholars, faculty members, and research scientists in modern approaches to predictive calculations for materials and particularly their electronic properties. The school is currently planned to take place June 14 to June 20, 2021 at the University of Texas - Austin. The organizers have flexible backup plans including conversion to a virtual summer school, to compensate for Covid-19 related contingencies. The focus of this summer school will be on calculating how electrons interact with oscillations of atoms in the crystal, or phonons starting from fundamental understanding of electrons and atoms, the building blocks of materials. The electron-phonon interaction plays an important in determining the temperature dependence of many electronic and optical properties of solids, and plays a central role in technologically important phenomena, from charge and heat transport to superconductivity and light-driven phase transitions. With rapid progress in materials design and data-driven materials discovery there is a growing need for more advanced computational methods that start from the atomic level together with their implementation in software to describe complex functional properties of materials and materials systems with predictive accuracy. This summer school will bring together expertise from across the nation and the world to introduce participants to advanced first principles methods for calculating electron-phonon physics and related materials properties, including lectures on the quantum mechanical theory of systems of many particles, software implementations, and hands-on training sessions. The school will be followed by a hackathon event guided by experts from the Texas Advanced Computing Center, and devoted to creating and maintaining sustainable cyberinfrastructure. There is currently no specialized training available in this area; this school fills a significant gap in the education of the next generation of physicists, chemists, materials scientists, and engineers.This school will contribute to developing a globally competitive STEM workforce by exposing participants to advanced techniques in computational materials modelling and design. By training participants into best practices in scientific computing and software development, the school will contribute to educating scientists and engineers that will go on to build tomorrow’s cyberinfrastructure. This school will also foster partnerships between academia and industry by delivering training in techniques that can be employed in an industrial setting, and by doing so it will contribute to increasing the economic competitiveness of the United States. Participation of underrepresented minorities, women, and persons with disabilities will be encouraged and prioritizedin order to increase diversity in STEM. An event dedicated to diversity and inclusion will be heldduring the school.TECHNICAL SUMMARYThis award supports a summer school activity to train graduate students, postdoctoral scholars, faculty members, and research scientists in modern approaches to predictive calculations for materials and particularly their electronic properties. The school is currently planned to take place June 14 to June 20, 2021 at the University of Texas - Austin. The organizers have flexible backup plans including conversion to a virtual summer school, to compensate for Covid-19 related contingencies. This school will introduce researchers to state-of-the art techniques for predictive first principles calculations of electronic, optical, and transport properties of materials at finite temperature. By the end of the school participants will be able to compute electron-phonon couplings, band structures including zero-point quantum fluctuations and temperature effects, optical properties including phonon-assisted quantum processes, critical temperature and superconducting gap of phonon mediated superconductors, electron and hole mobilities in semiconductors, the resistivity of metals, and polaronic properties. These properties are essential for designing the materials that will underpin future technology, including solar cells, displays, touch screens, superconducting cables, portable electronics, batteries, and quantum computers. There is currently no specialized training available in this area; this school fills a significant gap in the education of the next generation of physicists, chemists, materials scientists, and engineers.This school will contribute to developing a globally competitive STEM workforce by exposing participants to advanced techniques in computational materials modelling and design. By training participants into best practices in scientific computing and software development, the school will contribute to educating scientists and engineers that will go on to build tomorrow’s cyberinfrastructure. The school will also foster partnership between academia and industry by delivering training in techniques that can be employed in an industrial setting, and by doing so it will contribute to increasing the economic competitiveness of the United States. Participation of underrepresented minorities, women, and persons with disabilities will be encouraged and prioritized in order to increase diversity in STEM. An event dedicated to diversity and inclusion will be held during the school.This award by the Division of Materials Research within the NSF Directorate of Mathematical and Physical Sciences is jointly supported by the NSF Office of Advanced Cyberinfrastructure in the Directorate of Computer and Information Science and Engineering.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": 1405, "pages": 1424, "count": 14236 } } }