Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=-award_amount
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-award_amount", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-award_amount", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-award_amount", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1390&sort=-award_amount" }, "data": [ { "type": "Grant", "id": "12691", "attributes": { "award_id": "2223931", "title": "Collaborative Research: CEDAR--Exploring the Response of the Magnetosphere to Terrestrial Weather", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "MAGNETOSPHERIC PHYSICS" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28606, "first_name": "Kevin", "last_name": "Pham", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 275, "ror": "", "name": "University Corporation For Atmospheric Res", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The extent to which terrestrial meteorology can influence the Mesosphere-Ionosphere-Thermosphere system (MIT, ca. 70-500 km altitudes) across a wide range of spatial and temporal scales is an important discovery of the past two decades. Sudden stratospheric warmings (SSWs) are prominent events that couple dynamical variability in the lower atmosphere with upper atmosphere perturbations. This project will explore novel fundamental connections in the coupled MIT system leveraging the state-of-the-art and fully two-way coupled Multiscale Atmosphere-Geospace Environment (MAGE) model, and ground- (SuperMAG) and space-based (GOES-14 and -15) magnetic field observations. Understanding the sources of ionospheric and magnetospheric variability is a necessary first step in developing predictive capabilities of critical importance to the operation of navigation and communications systems that support our modern society. This research will result in a better understanding of how terrestrial weather interacts with the ionosphere to produce variability in electric fields that redistribute plasma at higher levels in the upper atmosphere. In addition, the research effort will broaden participation by involving and training a UCAR Significant Opportunities in Atmospheric Research and Science (SOARS) protégé from the historically underrepresented communities in a multi-year mentoring and career development experience. This collaborative award is aimed at establishing and quantifying the extent to which lower atmospheric forcing can impact the spatial and day-to-day variability of the magnetosphere and explore what coupling mechanisms may be at play. Three science questions will be investigated: 1) To what extent can Sudden Stratospheric Warmings (SSWs) impact the spatial-temporal variability of the upper ionosphere and magnetosphere? 2) How well does MAGE approximate the SSW-induced variability observed in the magnetosphere? And 3) What roles do large-scale waves play in dynamically coupling this lower atmospheric variability into the magnetosphere? This study will for the first time: (1) establish and quantify the extent to which large-scale lower atmospheric forcing can impact the spatial and temporal variability of the magnetosphere and (2) explore what coupling mechanisms may be at play and elucidate the contribution of lower atmospheric disturbances in coupling terrestrial weather with the entire MIT system, thus addressing outstanding issues of critical importance to both the CEDAR and GEM communities.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": "12692", "attributes": { "award_id": "2213295", "title": "LEAPS-MPS: Deep Learning the Knot Landscape", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "OFFICE OF MULTIDISCIPLINARY AC" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28607, "first_name": "Mark", "last_name": "Hughes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 436, "ror": "https://ror.org/047rhhm47", "name": "Brigham Young University", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole under the American Rescue Plan Act of 2021 (Public Law 117-2). The field of knot theory began in the mid 1800s, motivated heavily by ideas from physics. Today, knots play an important role in physics through gauge theory and quantum field theories, molecular biology via protein and DNA knotting, and low-dimensional topology in the form of handlebody diagrams of 4-manifolds and Dehn surgery descriptions of 3-manifolds. In 2016 the PI initiated a novel approach to studying problems in knot theory by applying techniques of machine learning and artificial intelligence. While there is now a small but growing body of research in this direction, with contributions from mathematicians, physicists, and computer scientists, most of the existing work focuses on techniques of supervised learning and applications of reinforcement learning to unknotting braids. The PI will adapt new techniques from generative machine learning and reinforcement learning to study topological properties of knots and learn latent distributions of knots and their invariants. The PI will also extend earlier work with collaborators, by establishing theoretical underpinnings of new observations and experimental results. As part of this project the PI will develop a mentored data science research training program for undergraduate students, in which students will be mentored by both the PI and a data scientist from industry or academia. Students will participate in a semester-long mentored learning group with the PI, before completing an intensive workshop where they work together to solve data science problems from industry with their external mentor. At each step of this program, special attention will be given to increasing participation of historically underrepresented groups through partnerships with campus organizations that specialize in outreach to these communities. By participating in mentored research these students will gain experience that will help them prepare for graduate degrees and careers in academia and industry, thereby preparing to be future role models for students from these underrepresented communities.The PI will adapt text-to-image generative adversarial networks to construct invariant-to-knot GANs, allowing for the construction of knots with prescribed topological properties. The PI will also use variational autoencoders to learn new latent distributions of knots which are natural with respect to various topological properties and invariants. These latent representations of knots will provide a clearer understanding of knot distributions and produce random models that allow for targeted generation of knots with specified properties. Any new latent representations of knot theoretic data will be made available to other researchers for use in training new machine learning models, improving the performance of the models being developed. These techniques will be used to guide searches for counterexamples to important open conjectures. In addition, the PI will use deep reinforcement learning algorithms to study the slice genus and braid band rank problems, generalizing existing results on the use of reinforcement learning to the unknotting of braids. Given that the problem of computing the slice genus of knots is central to key open questions in low-dimensional topology and constructions in physics, new techniques developed here will have direct applications outside of knot theory. Successful use of these techniques will serve as a template for future applications of generative machine learning and reinforcement learning to other areas of mathematics.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": "12693", "attributes": { "award_id": "2200622", "title": "EIR: A Unified Theoretical Framework for Zero Trust Architectures", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "HBCU-EiR - HBCU-Excellence in" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28608, "first_name": "Onyema", "last_name": "Osuagwu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 291, "ror": "https://ror.org/017d8gk22", "name": "Morgan State University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "Zero Trust, has generally been explained as a network in which capabilities and access among all of the participating systems are highly regulated or require a sufficiently high level of proof before permissions are granted for any period of time. As reassuring as these words are for many in this space, the implementation of such networks and architecture lags due to the lack of an rigorous ground truth for success. In other words, if you ask any number of people to show you how they ”implemented” their Zero Trust environment with the same initial specifications you will get at a minimum number of responses with varying levels of verifiable security. The multiple responses are not the problem in this case as much as the variability in the level of security due to the ill-posed question of trust in these systems. The failure to develop true resilience is strongly related to the lack of a unified theoretical framework born out of fundamental cybersecurity experiments and results. This work will first frame and identify the appropriate scale for the question of trust in the cybersecurity domain. The education and research goals of this project are designed to strongly support the engagement in the community.The proposed research task is to do the research and development of the mathematical rules and bounds, e.g., first-order logic, formal methods, etc. to accurately encapsulate all the requirements needed to achieve a “True Zero Trust” architecture for a networked environment. The second research challenge is to prototype, build, test and attack these “True Zero-Trust” networks and compare them to other standards. These research tasks require accurate, detailed, and reproducible testbed construction and validation paired with the architecture. They will use Amazon Web Services to design and test initial architectures across four phases. The third research challenge is to verify the “True Zero-Trust” architecture at scale during varied attack scenarios under high utilization stress. The fourth research challenge is to develop an “Equation of State” for these systems that provides a “Figure of Merit” when judging the security of these systems. This work is strongly aligned with the CISE directorate’s mission in particular the CCF program’s Foundations of Emerging Technology thrust and the SaTC program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12694", "attributes": { "award_id": "2240514", "title": "Collaborative Research: CISE-MSI: DP: CNS: An Edge-Based Approach to Robust Multi-Robot Systems in Dynamic Environments", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CISE MSI Research Expansion" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28527, "first_name": "Md Tanvir", "last_name": "Arafin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 291, "ror": "https://ror.org/017d8gk22", "name": "Morgan State University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Multi-robot systems consist of autonomous robots interacting in a shared environment to achieve common goals. They are widely used in real-world application domains such as transportation, disaster management, as well as warehousing and manufacturing. This project develops an efficient, robust, and secure multi-robot system, called EdgeRobot. EdgeRobot establishes an edge computing based architecture and algorithmic framework to facilitate multi-robot collaboration and coordination in dynamic environments. This work provides new model, architecture, and theory for coordinated multi-robot systems. In addition, this project builds research capacity, sustainable for training underrepresented students via the partnership of six geographically diverse minority-serving institutions in the United States: the University of Houston-Clear Lake (South), the University of Michigan Flint (North), CUNY-New York City College of Technology (Northeast), Morgan State University (East), San Francisco State University (West), and California State University Dominguez Hills (West). The cross-institutional collaboration not only boosts research capacity in all six participating institutions but also provides integrative research and education experience to their underrepresented minority students. Ultimately, this project establishes and exemplifies an effective collaboration model for training and educating underrepresented students from geographically diverse minority-serving institutions.This project consists of the following three research thrusts. First, the novel edge computing infrastructure provides optimal and location-aware computing services for collaborative robots to achieve their common goals. Besides, reinforcement learning-based algorithms solve the multi-robot scheduling and routing problems, modeled as variants of the prize-collecting traveling salesman problem. Second, in tasks requiring collaborative actions, such as cooperative target tracking, multi-agent reinforcement learning enables teams of robots to operate, learn, and adapt in dynamic and human-populated environments robustly and safely. Third, integrating modern cryptographic and security primitives secures the collaboration among edge nodes in multi-robot systems. Consequently, the interface between EdgeRobot and its human team members builds a shared autonomy model.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": "12695", "attributes": { "award_id": "2229064", "title": "Collaborative Research: SHINE: Investigation of Mini-filament Eruptions and Their Relationship with Small Scale Magnetic Flux Ropes in Solar Wind", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "SOLAR-TERRESTRIAL" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28609, "first_name": "Haimin", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 228, "ror": "https://ror.org/05e74xb87", "name": "New Jersey Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "It is well known that magnetic flux ropes (MFRs) exist ubiquitously in the solar surface and in the interplanetary space. MFRs are generally defined as a bundle of magnetic fields that are twisted about each other and wrap around a common axis. The large scale MFRs are often associated with solar filament eruptions, subsequent propagation of Coronal Mass Ejections (CMEs) in solar wind, and furthermore, the Interplanetary CMEs (ICMs) towards Earth. In recent years, small-scale MFRs (SMFRs) in both solar surface and solar wind are receiving significant attention. Initial evidences show that they are numerous and ubiquitous from high resolution observations from the NSF-funded 1.6m Goode Solar Telescope (GST) of Big Bear Solar Observatory (BBSO) and from NASA’s Parker Solar Probe (PSP). This project addresses the Solar, Heliospheric, and Interplanetary Environment (SHINE) goal of linking the generation and propagation of SMFRs from the solar surface to the solar wind. Two female early career researchers will be supported, as well as undergraduate, graduate, and high school students. The team will mentor students at the NSF REU sites of New Jersey Institute of Technology (NJIT) and the University of Alabama Huntsville (UAH).Using high-resolution, high-polarimetric and spectroscopic data from GST, in-situ observations from PSP as well as other ground-based and space observations, NJIT and UAH join an effort to carry out comprehensive case and statistical studies of SMFRs in solar surface and solar wind. The team will investigate the kinematic, thermal and magnetic properties of them, as well as possible photospheric magnetic field evolution associated with eruption of mini-filaments. In addition, they will find the connection between solar mini-filament eruptions and detected SMFRs in solar wind. The project uses data from NSF funded ground-based observations of BBSO. Combining the most advanced data from GST/BBSO and PSP, the team will disclose detailed properties of SMFRs and address the following key science questions. (1) What are the dynamic properties of mini-filament eruptions (velocity, temperature, density, energy)? (2) What is the photospheric magnetic structure and evolution associated with mini-filament eruptions? (3) What are the statistical distributions of mini-filament eruption in coronal holes and regular quiet sun? (4) Statistically, is there a possible connection between mini-filament eruptions and the SMFRs in the solar wind? These questions are of importance from two aspects: (1) to disclose the basic plasma properties of SMFRs and (2) to advance understanding of the formation of transients in the solar wind.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": "12696", "attributes": { "award_id": "2145736", "title": "CAREER: Learning Mechanisms from Single Cell Multi-Omics Data", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Innovation: Bioinformatics" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28610, "first_name": "Xiuwei", "last_name": "Zhang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 294, "ror": "", "name": "Georgia Tech Research Corporation", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Cells are the fundamental units of life. Understanding how cells differentiate into various cell types and how cells change during evolutionary process has been a long-standing scientific problem. Recent sequencing and imaging technologies can provide large scale data for each single cell in a tissue, including the amount of mRNA products of a gene, the amount of protein products of certain genes, the chromatin 3D structure, and the spatial location of each cell. The project will develop computational methods to learn mechanisms in cell differentiation and development from complex and high dimensional data. The project will provide open-source tools for the community and can be integrated with educational activities and outreach, such as courses on topics of algorithms in computational biology. Opportunities for high school and undergraduate students especially those from underrepresented groups will be provided to participate in cutting-edge research in computational biology.During recent years, single cell technologies have progressed in terms of both “modality” and “scale”. In terms of modality, multi-omic technologies allow researchers to profile each single cell from multiple aspects, including transcriptome, genome, chromatin accessibility and protein abundance. In terms of scale, more cells, tissues, individuals, and species are profiled with single cell RNA-sequencing (scRNA-seq) technology. Computational integration methods have been developed to integrate single cell data from different modalities or different batches. The goal of the project is to develop integration tools with a strong emphasis on mechanisms, and to learn molecular mechanisms from the large-scale, multi-modality single cell data. The specific aims are: (1) develop methods to integrate paired and unpaired single cell multi-omics data from the same tissue, and in particular, propose a method to learn consensus cell identity considering cross-modality relationships; (2) develop deep learning methods to integrate scRNA-seq data from multiple individuals or species. The method aims to remove technical batch effects but preserve biological variation between data matrices and infer the genes which are associated with each meta feature like age and race; (3) develop methods to learn cell-specific gene regulatory networks (GRNs) for cells in a temporal or spatial context. When spatial locations of cells are available, the project will implement a method to learn both cell-specific GRNs and cell-cell interactions. This research has the potential to make a significant step forward in understanding mechanisms in cells and diseases using large-scale, multi-modality single cell data. The results of the project can be found at the PI’s website: https://xiuweizhang.wordpress.com/.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": "12697", "attributes": { "award_id": "2144915", "title": "CAREER: Fossil Amber Insight Into Macroevolutionary Dynamics in an Ecologically Diverse Island System", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Cross-BIO Activities" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28611, "first_name": "Phillip", "last_name": "Barden", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 228, "ror": "https://ror.org/05e74xb87", "name": "New Jersey Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Fossils provide direct evidence for the processes that shape ecosystems and biodiversity over long periods of history on earth. In particular, the fossil record has helped us better understand past extinctions and ecological changes, which are increasingly relevant to a rapidly changing planet. At the same time, the fossil record is highly incomplete, and it can be difficult to unify our understanding of ecosystems across long periods of time spanning millions of years. This project seeks to synthesize information from data-rich fossil and modern communities in a unique island system to reveal broadly applicable patterns and processes that are responsible for maintaining biodiversity. This research focuses on ants and ant communities of Hispaniola, which are both preserved in high detail in the amber fossil record and highly diverse on the island today. This synthesis will address key questions including: Why do some organisms go extinct while others persist over long periods of time? How predictable are changes in ecosystems and communities across long periods of time? How quickly do these changes occur? Knowledge and data resulting from this research will be used in the creation of K-12 educational materials, which will be developed through interdisciplinary collaborations between industrial design and biology undergraduates. Educational products will be widely disseminated in schools and museums in the U.S. and Dominican Republic. The project will produce biological, ecological, and analytical resources for researchers locally and worldwide. It will provide research and educational opportunities at the high school, undergraduate, graduate, and postdoctoral levels with inclusive mentoring.The project will build a comprehensive dataset for the entire fossil and extant ant communities on Hispaniola, with a total of ~250 species collectively. Project activities will integrate taxonomy, systematics, morphology, high-resolution CT-scan imaging, genome-scale molecular data, phylogenetics, supervised machine learning, and phylogenetic comparative methods. Research is specimen-based and will utilize and contribute to multiple museum collections. Data derived from fossil and extant taxa will be used to test interrelated hypotheses related to extinction selectivity, faunal turnover, and the role of macroevolutionary processes in community assembly. All project products, including datasets, will be made publicly available, and open-access resources will be produced for future researchers. Educational products from the project comprise tactile lesson plans and active learning projects that will reach underserved communities and encourage participation in STEM career pathways. This inclusion work will be further supported through a high school to postdoctoral mentoring pipeline with the goal of recruiting and retaining diverse students in biological research.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": "12698", "attributes": { "award_id": "2218427", "title": "Identifying novel memory traces that improve action precision", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Perception, Action & Cognition" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28612, "first_name": "Maurice", "last_name": "Smith", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 455, "ror": "https://ror.org/03vek6s52", "name": "Harvard University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "How do we remember a phone number long enough to dial it or remember two numbers long enough to add them together in our head? This type of memory, called short-term working memory, is a cognitive store that allows a few items to be recalled and acted upon within a short span of time. Early studies of working memory suggested that we can typically remember about 5 to 9 unrelated items. More recent work suggests a true capacity of just 3-4 distinct items. A briefer but far more vivid form of short-term memory, called sensory memory, has been identified in the visual, auditory, and tactile systems. Visual sensory memory, for example, is remarkable in that it can provide incredibly detailed information about 64 or more items in recent visual scenes, a far greater capacity than shown for working memory. The present work will study a previously unidentified proprioceptive sensory memory (proprioception refers to information about the position and movement of the body). Like visual sensory memory, proprioceptive sensory memory provides a high-precision but short-lasting store for sensory information and for information about recent motor actions. The hypothesis is that both type of memories, when available, provide high-precision input into the sensorimotor neural circuitry involved in action planning, allowing for extremely high levels of motor precision.The goals of this proposal are to develop an understanding of the relationship between the availability of novel high-precision proprioceptive and motor command memories and the spatiotemporal properties of improvements in motor precision. The team will begin by identifying the existence of a high-precision sensory memory for proprioception by determining the link between the availability of this novel memory and improved action precision. They will then characterize the extent and time course of the rapid reduction in time scale variability that this memory can provide. Finally, the research will parcel out two novel high-precision hyper-transient memories, proprioceptive sensory memory and motor command memory, based on both spatial and temporal properties, using geometric characterization and direct experimental manipulation. The planned work will develop a framework for understanding how recent sensory and motor memories can work both separately and in combination to improve motor precision during voluntary movement. This project provides a fertile research training opportunity to apply computational and engineering principles and tools to the study of learning and memory in neural circuits for trainees ranging from the undergraduate to the postdoctoral level, and introduces community middle school and high school students to how learning and memory shape the ability to precisely control actions in humans.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": "12699", "attributes": { "award_id": "2240517", "title": "Collaborative Research: CISE-MSI: DP: CNS: An Edge-Based Approach to Robust Multi-Robot Systems in Dynamic Environments", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CISE MSI Research Expansion" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28613, "first_name": "Bin", "last_name": "Tang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1204, "ror": "", "name": "California State University-Dominguez Hills Foundation", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Multi-robot systems consist of autonomous robots interacting in a shared environment to achieve common goals. They are widely used in real-world application domains such as transportation, disaster management, as well as warehousing and manufacturing. This project develops an efficient, robust, and secure multi-robot system, called EdgeRobot. EdgeRobot establishes an edge computing based architecture and algorithmic framework to facilitate multi-robot collaboration and coordination in dynamic environments. This work provides new model, architecture, and theory for coordinated multi-robot systems. In addition, this project builds research capacity, sustainable for training underrepresented students via the partnership of six geographically diverse minority-serving institutions in the United States: the University of Houston-Clear Lake (South), the University of Michigan Flint (North), CUNY-New York City College of Technology (Northeast), Morgan State University (East), San Francisco State University (West), and California State University Dominguez Hills (West). The cross-institutional collaboration not only boosts research capacity in all six participating institutions but also provides integrative research and education experience to their underrepresented minority students. Ultimately, this project establishes and exemplifies an effective collaboration model for training and educating underrepresented students from geographically diverse minority-serving institutions.This project consists of the following three research thrusts. First, the novel edge computing infrastructure provides optimal and location-aware computing services for collaborative robots to achieve their common goals. Besides, reinforcement learning-based algorithms solve the multi-robot scheduling and routing problems, modeled as variants of the prize-collecting traveling salesman problem. Second, in tasks requiring collaborative actions, such as cooperative target tracking, multi-agent reinforcement learning enables teams of robots to operate, learn, and adapt in dynamic and human-populated environments robustly and safely. Third, integrating modern cryptographic and security primitives secures the collaboration among edge nodes in multi-robot systems. Consequently, the interface between EdgeRobot and its human team members builds a shared autonomy model.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": "12700", "attributes": { "award_id": "2240516", "title": "Collaborative Research: CISE-MSI: DP: CNS: An Edge-Based Approach to Robust Multi-Robot Systems in Dynamic Environments", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CISE MSI Research Expansion" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-09-01", "end_date": null, "award_amount": 0, "principal_investigator": { "id": 28614, "first_name": "Lili", "last_name": "Ma", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1020, "ror": "", "name": "CUNY New York City College of Technology", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Multi-robot systems consist of autonomous robots interacting in a shared environment to achieve common goals. They are widely used in real-world application domains such as transportation, disaster management, as well as warehousing and manufacturing. This project develops an efficient, robust, and secure multi-robot system, called EdgeRobot. EdgeRobot establishes an edge computing based architecture and algorithmic framework to facilitate multi-robot collaboration and coordination in dynamic environments. This work provides new model, architecture, and theory for coordinated multi-robot systems. In addition, this project builds research capacity, sustainable for training underrepresented students via the partnership of six geographically diverse minority-serving institutions in the United States: the University of Houston-Clear Lake (South), the University of Michigan Flint (North), CUNY-New York City College of Technology (Northeast), Morgan State University (East), San Francisco State University (West), and California State University Dominguez Hills (West). The cross-institutional collaboration not only boosts research capacity in all six participating institutions but also provides integrative research and education experience to their underrepresented minority students. Ultimately, this project establishes and exemplifies an effective collaboration model for training and educating underrepresented students from geographically diverse minority-serving institutions.This project consists of the following three research thrusts. First, the novel edge computing infrastructure provides optimal and location-aware computing services for collaborative robots to achieve their common goals. Besides, reinforcement learning-based algorithms solve the multi-robot scheduling and routing problems, modeled as variants of the prize-collecting traveling salesman problem. Second, in tasks requiring collaborative actions, such as cooperative target tracking, multi-agent reinforcement learning enables teams of robots to operate, learn, and adapt in dynamic and human-populated environments robustly and safely. Third, integrating modern cryptographic and security primitives secures the collaboration among edge nodes in multi-robot systems. Consequently, the interface between EdgeRobot and its human team members builds a shared autonomy model.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": 1391, "pages": 1419, "count": 14184 } } }