Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1397&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1390&sort=-keywords" }, "data": [ { "type": "Grant", "id": "10159", "attributes": { "award_id": "2111522", "title": "Optimality and Robustness in Piecewise-Deterministic Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "COMPUTATIONAL MATHEMATICS" ], "program_reference_codes": [], "program_officials": [ { "id": 1114, "first_name": "Leland", "last_name": "Jameson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2024-08-31", "award_amount": 466802, "principal_investigator": { "id": 26074, "first_name": "Alexander", "last_name": "Vladimirsky", "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 project focuses on quantifying and actively managing uncertainties resulting from random switches in global environments. An abrupt ecological change, a new disruptive technology, a global economic downturn, or an emerging pandemic – any such game-changer event may transform the planning environment and shift the priorities of decision makers. Reactive planning is often the norm in practice, with the working assumption that global mode switches are too rare and unlikely to take them into account. But if a statistical characterization of such switches is available, using it in strategic planning can significantly improve the performance of the controlled system. Models with these features arise naturally in many research areas including economics, behavioral ecology, public policy, robotic navigation, evolutionary biology, and security applications (e.g., preventing illegal logging or wildlife poaching). PI will develop efficient numerical methods for controlling such systems, focusing on trade-offs between the average-case optimality and robustness (interpreted as minimizing the probability of undesirable outcomes.) This project will support 2 graduate students in each of the first two years and 1 graduate student in the third year. \n\nPiecewise-Deterministic Markov Processes (PDMPs) provide an excellent framework for modeling large-scale stochastic perturbations of the global environment. The aleatoric uncertainty due to such perturbations is an important feature of realistic control problems, but until recently it has attracted far less attention in mathematical literature than the diffusive perturbations studied in ``classical'' stochastic optimal control theory. Practitioners often want to model these environment-switch uncertainties as well as time-structured information accumulation patterns present in their applications. Moreover, it may not be enough for them to optimize the expected value of the outcome. They often need to maximize the probability of good outcomes while imposing constraints on the worst-case scenario. To accomplish this, we need to modify the partial differential equations (PDEs) encoding the optimal behavior, and this presents a range of new computational challenges: free boundaries, discontinuities, higher dimensionality of the state space, and larger systems of coupled nonlinear PDEs. We propose to study the trade-offs involved in using such modified models and to develop numerical methods to solve them efficiently. In the PDMP setting, even the traditional risk-neutral approach of optimizing the expected performance can be computationally costly since it involves solving a coupled system of Hamilton-Jacobi-Bellman (HJB) equations. We develop several approaches for decreasing this computational cost by constructing new discretization schemes and leveraging efficient methods previously developed for fully deterministic problems. We also extend our recent approach for optimizing the Cumulative Distribution Function (CDF) of the total cost incurred by a stochastic switching system. This is accomplished by solving a different system of HJB equations on an expanded state space, with ``threshold-optimal'' controls recovered for all starting configurations and all threshold values simultaneously. We further investigate the trade-offs between conflicting optimization criteria and several notions of robustness.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10160", "attributes": { "award_id": "2106155", "title": "IRES Track II - Cape Horn ASIs: Climate change and disease ecology at the southern end of the Americas", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office Of The Director", "IRES ASI - Track II: IRES Adva" ], "program_reference_codes": [], "program_officials": [ { "id": 2094, "first_name": "Fahmida", "last_name": "Chowdhury", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2024-08-31", "award_amount": 396610, "principal_investigator": { "id": 26077, "first_name": "Andrew", "last_name": "Gregory", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26075, "first_name": "James H", "last_name": "Kennedy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26076, "first_name": "Ricardo", "last_name": "Rozzi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 253, "ror": "https://ror.org/00v97ad02", "name": "University of North Texas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Over sixty percent of Earth’s ice-free areas are best described as human-modified anthropogenic biomes. This means that pristine areas are scarce, and that most ecological studies are de facto studies of anthropogenic impacts on ecological systems. However, the study of pristine systems is necessary for developing a baseline understanding of ecological function so that we can apply this understanding to develop meaningful and appropriate conservation plans, or even know what applied research questions are worth pursuing. Unfortunately, pristine systems are at a premium. In general, graduate students do not have the opportunity to develop their ecological understanding under pristine or near pristine ecological systems. In addition, the National Academy of Science, Engineering and Medicine data suggest that current STEM graduate student training may not be providing a rigorous education in quantitative experimental design. Quantitative experimental design is designing studies with an eye toward the statistical analyses that will be used to analyze the data. Using quantitative design principles, rooted in sound ecological theory, ensures that researchers are asking relevant and testable questions that will lead to meaningful results. This is a crucial component of graduate student education and researcher professional development, as it ensures that increasingly scarce research funding are not wasted Additionally, collaboration is becoming the norm in research, and international collaboration via electronic media was becoming increasingly common even before the current pandemic. In this IRES Track II project, students will have the opportunity to be trained in quantitative experimental design and work as part of a multinational research collaboration to study infectious disease emergence in one of the few remaining pristine places on Earth. The Cape Horn Biosphere Reserve (CHBR) off the southern tip of South America protects pristine ecosystem is perfect for this type of graduate student training. The CHBR is part of the sub-Antarctic Magellanic ecoregion, which is globally significant because it houses the worlds southernmost forest biome, contains numerous endemic species, is remote, and is relatively free of anthropogenic impacts. In addition, with the exception of Avian Malaria which arrived just a few years ago, the system is relatively free of zoonotic diseases. Making the region a living laboratory to study the ecosystem transformations that co-occur with human discovery and settlement of an area. \n\nThe program will bring graduate students from across the US to work in international collaborative teams with Chilean students from the Universidad de Magallanes (UMAG) and the Institute of Ecology and Biodiversity (IEB) in Chile, and a diverse group of faculty from across the US and Chile. The focus of the program will to be provide graduate students robust and hands-on training in quantitative experimental design and international collaboration resulting in publications. The research questions themselves will be flexible, but organized thematically. Specifically, this IRES Track-II will focus on the merging molecular genetic analysis using a mobile next generation sequencing lab with mist netting and arthropod trapping to investigate the impacts of wildlife disease on local biodiversity and community structure. Secondarily, eDNA and traditional wildlife disease monitoring approaches will be applied to understand the potential for zoonosis and understanding ecological factors that contribute to, or inhibit, zoonosis. Resultantly, participation in this program will help train the next generation of scientists with the skills needed to make meaningful contributions to the study of ecology, conservation, and wildlife disease ecology.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10161", "attributes": { "award_id": "2049164", "title": "Taking Large-Scale Surveys into the Future", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Sociology" ], "program_reference_codes": [], "program_officials": [ { "id": 741, "first_name": "Melanie", "last_name": "Hughes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": "2023-08-31", "award_amount": 979923, "principal_investigator": { "id": 26078, "first_name": "David", "last_name": "Cantor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1891, "ror": "", "name": "Westat Inc", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "Society has come to rely on large-scale surveys of human populations. Until recently, in-person surveys have been seen as the ‘gold standard,’ primarily because of their high response rates. However, cooperation with such surveys has been declining dramatically. At the same time, an increasing share of the U.S. population has gained access to the internet, which has given rise to web surveys. Such surveys are much less expensive than in-person ones, are not vulnerable to interruptions related to events like pandemics, and are not subject to errors that interviewers may introduce. At present, however, our understanding of the trade-offs between in-person and web administration for lengthy surveys is not well developed. The project assesses the impact of converting the in-person General Social Survey -- an essential part of the data infrastructure for social science in the United States -- to a state-of-the-art web survey. The results will be of significant value to survey researchers. \n\t\nThe project conducts an experimental study of the effects of survey mode and delivery format using the 2022 General Social Survey (GSS) questionnaire. A nationally representative sample of households will be randomly assigned to one of two different versions of the web survey. One version will be a single long instrument. The other version will break up the 90-minute GSS into three parts, asking respondents to complete each module at three separate times. The goal is to reduce respondent burden by breaking up the respondent’s task into more manageable pieces. The web surveys will be administered at approximately the same time as the 2022 in-person GSS. Comparison of the in-person GSS to the long web survey will measure the effect of mode of administration. Comparison of the long versus the modular web surveys will provide a measure of the effects of breaking up the survey into shorter tasks. Analysis will compare the three surveys by: 1) response rates and indicators of non-response bias; 2) indicators of the response process and data quality; 3) GSS demographic, behavioral, and opinion measures; and 4) costs.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10162", "attributes": { "award_id": "2115134", "title": "CICI: UCSS: ACSP4HR: Assuring Cyber Security and Privacy for Human Resilience Research: Requirements, Framework, Architecture, Mechanisms and Prototype", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Cybersecurity Innovation" ], "program_reference_codes": [], "program_officials": [ { "id": 2178, "first_name": "Rob", "last_name": "Beverly", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-07-01", "end_date": "2024-06-30", "award_amount": 499094, "principal_investigator": { "id": 6305, "first_name": "Shouhuai", "last_name": "Xu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 16248, "first_name": "Charles C", "last_name": "Benight", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26079, "first_name": "Yanyan", "last_name": "Zhuang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 490, "ror": "", "name": "University of Colorado at Colorado Springs", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "Citizens live in a world of prevalent adversities, ranging from childhood trauma, to domestic violence, war, flooding, hurricanes, wildfires, terrorist attacks, acute physical injuries, motor vehicle accidents, and pandemics. These adversities affect many lives; for example, during the past decade alone, unprecedented flooding, catastrophic hurricanes, and devastating wildfires destroyed thousands of homes, vital infrastructure, and communities. Citizens often struggle to cope with these adversities due to the trauma experienced during these events as well as the extensive recovery stress. The Human Resilience research community conducts scientific studies to (i) understand how citizens suffer from these adversities and (ii) seek effective solutions to help citizens recover or bounce back from the stress and trauma caused by these adversities. The importance of Human Resilience research can hardly be overstated because of the large population of citizens affected. Despite this clear importance, the scientific discovery process of Human Resilience research is hindered by the lack of cyberinfrastructure support for secure, privacy-preserving, and policy-complying data sharing that would foster collaborative research. This calls for solutions to modernizing and accelerating the scientific discovery process of Human Resilience research.\n\nThis project investigates a competent technical solution to modernizing and accelerating the scientific discovery process of Human Resilience research, by tackling a range of technical challenges including: (i) specifying the requirements of a competent cyberinfrastructure; (ii) defining a solution framework to adequately address these requirements; (iii) designing a comprehensive system architecture to fulfill the framework; (iv) investigating novel mechanisms and supporting techniques; and (v) developing a prototype system and demonstrating its usefulness. The project bolsters the scientific collaboration in the Human Resilience research community by encouraging the adoption of security and privacy into its unique scientific workflows and by pioneering a holistic security cyberinfrastructure environment spanning the entire Human Resilience research data-sharing ecosystem.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10163", "attributes": { "award_id": "2111277", "title": "Collaborative Research: Particles and Proxies for Sampling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "COMPUTATIONAL MATHEMATICS" ], "program_reference_codes": [], "program_officials": [ { "id": 6669, "first_name": "Yuliya", "last_name": "Gorb", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": "2024-07-31", "award_amount": 149999, "principal_investigator": { "id": 26080, "first_name": "David", "last_name": "Aristoff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 323, "ror": "https://ror.org/03k1gpj17", "name": "Colorado State University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "This project addresses sampling in high dimensions which is important for a variety of disciplines, including computational chemistry, materials science, and molecular dynamics simulations for climate models, power network, traffic models, or the study of viruses and pandemics. The project will develop new simulation algorithms as well as improvements of existing algorithms. The outcomes will benefit these disciplines in several ways. First, the algorithmic optimizations will provide new tools that practitioners could use to accelerate their computations. Second, rigorous results on these methods will provide practitioners with confidence in their predictions. Finally, open source software will be developed. Students will be involved and receive interdisciplinary training.\n \nThe project addresses challenges in sampling and related problems arising from complex energy landscapes such as in potential energy in an atomistic system; the negative log-likelihood in a Bayesian inference problem; or the loss function in a machine learning problem. In Markov Chain Monte Carlo methods, these landscapes often define the evolution of a Markov chain that samples some target distribution. This project will develop efficient computations of ergodic averages over Markov chains and methods that reduce the computational cost of ergodic averages, by either reducing the number of required iterations or reducing the per-iterate cost. The new techniques and analyses will be based on proxy landscapes and interacting particle systems. Proxies can reduce per-iterate cost or lead to faster convergence, while interacting particle systems can reduce the bias from proxies or cut down on variance. The project includes a study of how parameter choices affect the variance of the weighted ensemble particle method at finite particle number; the development of a weight-corrected particle system to account for bias from proxies; and an analysis of methods for overcoming sampling difficulties associated with rough landscapes.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10164", "attributes": { "award_id": "2105726", "title": "IGE: Stakeholder-Driven Sustainable Development Experiences for Enhancing STEM Graduate Education", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "NSF Research Traineeship (NRT)" ], "program_reference_codes": [], "program_officials": [ { "id": 4582, "first_name": "Daniel", "last_name": "Denecke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-06-15", "end_date": "2024-05-31", "award_amount": 458240, "principal_investigator": { "id": 26082, "first_name": "Rachel", "last_name": "Brennan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26081, "first_name": "Meng", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Graduate students trained in science, technology, engineering, and math (STEM) are essential for supporting innovation in sustainable development strategies, particularly for enhancing resiliency to unprecedented global challenges such as climate change, human migration, and pandemics. However, there is growing concern that STEM graduate education may be falling short on preparing the nation’s workforce to face these global challenges. Equipping students with transformative skills in STEM is necessary for their future success, and for the sustainable development of society. This National Science Foundation Innovations in Graduate Education (IGE) award to Pennsylvania State University will engage underrepresented graduate students in real-world international experiences. Students will develop creative solutions to complex sustainable development challenges as an integral part of team working in close collaboration with local communities. Each year, a cohort of 12 graduate students will advance technical, ecological, and social strategies for sustainable development in 1- to 6-month research internships at different sites around the world. To empower them to effectively and compassionately advocate for change where it is needed most, participants will also receive modular professional development training in science communication, multiculturalism, global leadership, and environmental justice. As a result, students will be better prepared to pursue diverse professional careers in sustainable development, increasing the capacity of the future workforce to solve important environmental challenges. \n\nThe overarching goal of this IGE project is to improve the scientific basis of environmental decision-making, and increase the likelihood of successful implementation of sustainability strategies now and into the future. To meet this goal, this program aims to advance project-based learning through hypothesis-driven contextual systems research in sustainable development. Collaborative, stakeholder-driven research topics intersect at the water-energy-food nexus and include: water treatment and reuse; renewable energy production; nutrient management; sustainable agricultural systems; reducing air pollution and carbon emissions; remote sensing for environmental impact assessment; and climate change mitigation. Sustainable development provides a unifying and motivational theme under which faculty and students can work together across traditional academic boundaries, providing inherent opportunities for teamwork and transdisciplinary collaboration. This initiative is strategically designed to train and empower students that are members of underrepresented groups to develop and mobilize sustainable development solutions together with stakeholders and leading experts. Further, this project will build the foundational principles of transformative graduate education in sustainability that can be replicated at community-engaged institutions everywhere. \n\nThe Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10165", "attributes": { "award_id": "2111278", "title": "Collaborative Research: Particles and Proxies for Sampling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "COMPUTATIONAL MATHEMATICS" ], "program_reference_codes": [], "program_officials": [ { "id": 6669, "first_name": "Yuliya", "last_name": "Gorb", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-01", "end_date": "2024-07-31", "award_amount": 149687, "principal_investigator": { "id": 26083, "first_name": "Gideon", "last_name": "Simpson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 377, "ror": "https://ror.org/04bdffz58", "name": "Drexel University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project addresses sampling in high dimensions which is important for a variety of disciplines, including computational chemistry, materials science, and molecular dynamics simulations for climate models, power network, traffic models, or the study of viruses and pandemics. The project will develop new simulation algorithms as well as improvements of existing algorithms. The outcomes will benefit these disciplines in several ways. First, the algorithmic optimizations will provide new tools that practitioners could use to accelerate their computations. Second, rigorous results on these methods will provide practitioners with confidence in their predictions. Finally, open source software will be developed. Students will be involved and receive interdisciplinary training.\n \nThe project addresses challenges in sampling and related problems arising from complex energy landscapes such as in potential energy in an atomistic system; the negative log-likelihood in a Bayesian inference problem; or the loss function in a machine learning problem. In Markov Chain Monte Carlo methods, these landscapes often define the evolution of a Markov chain that samples some target distribution. This project will develop efficient computations of ergodic averages over Markov chains and methods that reduce the computational cost of ergodic averages, by either reducing the number of required iterations or reducing the per-iterate cost. The new techniques and analyses will be based on proxy landscapes and interacting particle systems. Proxies can reduce per-iterate cost or lead to faster convergence, while interacting particle systems can reduce the bias from proxies or cut down on variance. The project includes a study of how parameter choices affect the variance of the weighted ensemble particle method at finite particle number; the development of a weight-corrected particle system to account for bias from proxies; and an analysis of methods for overcoming sampling difficulties associated with rough landscapes.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10166", "attributes": { "award_id": "2047856", "title": "CAREER: Deep representation learning for exploration and inference in biomedical data", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Info Integration & Informatics" ], "program_reference_codes": [], "program_officials": [ { "id": 1357, "first_name": "Wendy", "last_name": "Nilsen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2026-09-30", "award_amount": 586187, "principal_investigator": { "id": 26084, "first_name": "Smita", "last_name": "Krishnaswamy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "Biological systems are inherently complex. Increasingly sophisticated technologies are\nbeing used in biomedical science in order to make sense of this complexity and to understand the\nunderlying factors that cause disease. These technologies generate vast amounts of data in many\ndifferent forms, from changes in how genes and proteins are expressed in individual cells over time,\nto detailed clinical imaging data on large patient populations and whole genome sequencing studies\nacross hundreds of thousands of people. These newly developed datatypes could help uncover\nimportant mechanisms and pathways that underpin health and disease. However, there is a large\ngap between the information contained in these datasets and the ability to extract meaningful\ninsights. Here the PI proposes to address this by developing new machine learning approaches based\non mathematical foundations that will allow us to make sense of these complex datasets. The PI will develop deep representation learning techniques that focus on gaining overall insight into the\nstructures, dynamics, interactions, and predictive features of the data, and will allow \nspecific hypotheses regarding the underlying regulatory mechanisms that drive disease in different\ncontexts to derived. The proposal will also involve training a postdoc, graduate student, and mentorship of local high school students. In addition, it will enable the development of an online workshop to\nwidely disseminate knowledge of unsupervised data analysis to a diverse array of participants from\nacross the country.\n\nThis project proposes to advance biomedical data analysis via three main thrusts. The first thrust is focused on forming deep multiscale representations of the data based on data geometry, graph signal processing, and topological concepts, in combination with powerful, deep learning systems. Such representations will allow for exploration of structure and meaningful, predictive abstractions of the data in a scalable fashion. Our second thrust is focused on integrating multiple modalities of data and organizing multitudes of related datasets using optimal transport and generative models to gain insight into entire cohorts of patients or perturbation conditions. Our third thrust is focused on learning high dimensional stochastic dynamics of the data using neural SDE (stochastic differential equation) and graph ODE (ordinary differential equation) networks to gain insight into underlying gene regulatory networks. We apply our approaches in the context of several specific biomedical challenges. Achieving these aims will enable integration and exploration of a large volume of data for explaining underlying regulatory mechanisms and dynamic phenotypic changes.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10167", "attributes": { "award_id": "2111114", "title": "Collaborative Research: Strategic Course-based Adaptations of an Ecological Belonging Intervention to Broaden Participation in Engineering at Scale", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 604, "first_name": "Eric", "last_name": "Sheppard", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2025-09-30", "award_amount": 1731741, "principal_investigator": { "id": 26087, "first_name": "Linda", "last_name": "DeAngelo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26085, "first_name": "Kevin", "last_name": "Binning", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26086, "first_name": "Natascha T", "last_name": "Buswell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 272, "ror": "https://ror.org/01an3r305", "name": "University of Pittsburgh", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to serve the national interest by increasing the number and diversity of engineers produced in the United States. Science overall has made great progress in increasing the participation of women. However, engineering has made no overall progress in the last 20 years, with women continuing to earn only 20% of engineering bachelor’s degrees each year. Improving retention in the first two years of engineering programs is important in addressing ongoing attrition. In particular, engineering will become more inclusive when the concerns that many students have about feeling alone in thinking they are incapable of mastering the course’s content are addressed. In this project, short interventions will be implemented. These interventions are intended to reveal to students that most of the students in their class have these same concerns, that previous students just like them with similar concerns have successfully completed this coursework, and that their instructor believes they are capable of succeeding. Prior research by the project team using the intervention in first-year courses has shown that these interventions can entirely eliminate course retention differences by gender as well as by race/ethnicity. A new method for customizing this intervention will be developed, tested, and further improved so that it can have similar strong benefits in different courses and at different universities. Simple interventions that can be easily and scientifically customized to many contexts may have potential for significantly improving engineering outcomes across the United States.\n\nThis project uses an ecological-belonging intervention approach that only requires a one-class or one-recitation session to implement and has been shown to erase long-standing achievement gaps by gender and race/ethnicity in several introductory STEM courses. However, while simple, the intervention cannot involve a fixed script for different university and course contexts. Rather, the content of the intervention needs to be customized to the local context in order to address the specific concerns students have in that specific context. This project brings a highly interdisciplinary team across three strategically-selected universities with the goal of developing an approach to identify which 1st and 2nd year courses need this intervention, reveal student concerns in that course, adapt the intervention to address those concerns, and address other pragmatic constraints of how that course is taught. This systematic approach also includes processes for onboarding all the instructors of the given course. In answering a set of seven core research questions, the project intends to expand knowledge about 1) where (on which outcome variables), when (in which contexts, for which students), and why the ecological belonging intervention has positive effects, and 2) the extent to which this intervention on its own has measurable impacts on the overall problem of representation in the larger challenge of representation within the large engineering pathways that have struggled with representation. This kind of foundational knowledge is critical to making decisions about when to apply the intervention as well as providing important insights into how to apply the intervention. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10168", "attributes": { "award_id": "2114791", "title": "FW-HTF-P/Collaborative Research: Exploring Tools to Help Workers and Organizations Adapt to AI-enabled Robots", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "FW-HTF Futr Wrk Hum-Tech Frntr" ], "program_reference_codes": [], "program_officials": [ { "id": 2859, "first_name": "Balakrishnan", "last_name": "Prabhakaran", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": "2021-12-31", "award_amount": 77198, "principal_investigator": { "id": 8917, "first_name": "Erik", "last_name": "Brynjolfsson", "orcid": null, "emails": "", "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": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will promote exploration of scalable tools to aid workers and organizations adapt to artificially-intelligent robots. In a sharp departure from current robotic systems that have to be programmed for a single manipulation task in a very tightly constrained set of conditions, venture-funded firms are designing and beginning to test qualitatively new robotic technologies that promise to flexibly automate entire classes of embodied tasks in widely divergent conditions. In this likely future, robots will adapt as readily to new repetitive manual tasks as a modern microprocessor adapts to new computational tasks. Such \"learning\" robots would clearly have profound implications for workers and organizations, but previous research on automation offers only limited guidance on how they will adapt. The researchers have recently begun a nationwide, four-year field study that will identify edge cases in which organizations and low-skill workers achieve unlikely yet systematic success, given the introduction of this disruptive technology. This will allow deriving design constraints for potential solutions from grounded theory, centering on the hard-won, demonstrably successful innovations of a suitably-diverse pool of informants. While existing research stands to unveil the mechanisms behind rare, in vivo learning successes to the world, this FW-HTF-P (Future of Work at the Human-Technology Frontier - Planning) award will assemble a world-class team of researchers who are committed to trying to expand and capitalize upon these mechanisms via new tools. This research has high-impact potential for organizations, lower-skilled workers and policy makers on how to expand and enrich work involving increasingly intelligent systems in the 21st century.\n\nWith AI in robotics as the technology, humans collaborating with robots as the workers, and organizations employing both the robots and the workers as the context of work, the team of researchers will specifically contact and convene a group of top experts in diverse technical domains including social media, massive open online courseware, crowdsourced knowledge repositories, peer assessment and coaching, user experience design and platforms for on-demand labor, crowdsourcing and innovation challenge execution. Beyond these technical disciplines, the researchers will invite policymakers and technologists, as the pathways to local success will likely be deeply intertwined with legal and commercialization processes. The researchers will begin by sharing very preliminary findings, research questions and objectives from the current study with a select group of such researchers who may have interest in a potential collaboration. The researchers will then extend formal invitations to a workshop to no more than ten potential collaborators. This workshop will be one day in length and will be described as an opportunity to explore and decide upon potential collaborative opportunities related to helping workers and organizations adapt more productively to general-purpose robots. The researchers will explore potentially new organizational theories that take perspectives such as: (a) accounting for success as a learning problem in which robots, workers and organizations learn from each other; (b) the character of learning infrastructures evident in various practices for adapting to learning machines acting as co-workers; (c) how the organization of such learning practices impacts skill changes, role transformations, as well as workers and organizations. The researchers will then solicit participants' input and commitment for tools to scale the successes inherent in the findings and select the tool likely to have the greatest benefit for the most Americans. The researchers will then jointly craft an FW-HTF-R (Future of Work at the Human-Technology Frontier - Research) proposal with interested collaborators that reflects a rigorous test of this tool in real-world settings. The ultimate goal of this project is to develop the necessary research personnel, research infrastructure, and foundational work to expand the opportunities for studying future technology, future workers, and future work at the level of a FW-HTF full research proposal.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1391, "pages": 1397, "count": 13961 } } }