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48,453 grants matching “machine learning”
Optoretinography: All-optical measures of functional activity in the human retina
$1,037,920Ramkumar Sabesan · University Of Washington · U01 · FY2021 · EY
Computational Modeling Core
$1,037,741Katrina M. Waters · University Of Wisconsin-Madison · U19 · FY2017 · AI
Knowledge Management Center for Illuminating the Druggable Genome
$1,037,175Jeremy S Edwards · University Of New Mexico Health Scis Ctr · U24 · FY2021 · CA
ITR-(ASE+ECS)-(soc+sim+int)-Natural Language Processing Technology for Guided Study of Bioinformatics
$1,037,002Dan Roth · University Of Illinois At Urbana-Champaign · · FY2004 · CSE
A CORE FUNCTION OF THE AIST ANALYTIC CENTER FRAMEWORK IS TO FACILITATE RESEARCH AND ANALYSIS THAT USES THE FULL SPECTRUM OF DATA PRODUCTS AVAILABLE IN ARCHIVES HOSTING RELEVANT PUBLICLY AVAILABLE DATA. KEY TO THIS IS MAKING DATA FAIR (FINDABLE ACCESSIBLE INTEROPERABLE AND REUSABLE) NOT JUST FOR HUMANS BUT FOR AUTOMATED SYSTEMS. EFFECTIVE DATA DISCOVERY SERVICES AND FULLY AUTOMATED MACHINE-DRIVEN TRANSACTIONS REQUIRE METADATA THAT CAN BE UNDERSTOOD BY BOTH HUMANS AND MACHINES. BUT SUCH METADATA ARE UNCOMMON. MORE COMMONLY METADATA RECORDS ARE INADEQUATELY CONTEXTUALIZED INCOMPLETE OR SIMPLY DO NOT EXIST. WHEN THEY DO EXIST THEY OFTEN LACK THE SEMANTIC UNDERPINNINGS TO MAKE THEM MEANINGFUL. THE GOAL OF THE AUTOMATED METADATA PIPELINE (AMP) PROJECT IS TO (1) DEVELOP A FULLY-AUTOMATED METADATA PIPELINE THAT INTEGRATES MACHINE LEARNING AND ONTOLOGIES TO GENERATE SYNTACTICALLY AND SEMANTICALLY CONSISTENT METADATA RECORDS THAT ADVANCE FAIR OBJECTIVES AND SUPPORT EARTH SCIENCE RESEARCH FOR A DIVERSE GROUP OF STAKEHOLDERS RANGING FROM SCIENTISTS TO POLICY MAKERS AND (2) DEMONSTRATE THE APPLICATION OF AMP-ENHANCED DATA TO AUTOMATE AND SUBSTANTIALLY IMPROVE THE USE AND REUSE OF NASA-HOSTED DATA IN ENVIRONMENTAL/EARTH SYSTEMS MODELS IN THE ARTIFICIAL INTELLIGENCE FOR ECOSYSTEM SERVICES (ARIES VILLA ET AL. 2014) PLATFORM A DISTRIBUTED NETWORK OF ECOSYSTEM SERVICES AND EARTH SCIENCE MODELS AND DATA THAT RELIES ON SEMANTICS TO ASSEMBLE NETWORK-AVAILABLE DATA AND MODEL COMPONENTS INTO ECOSYSTEM SERVICES MODELS BUILT ON DEMAND AND OPTIMIZED FOR THE CONTEXT OF APPLICATION. ARIES IS A CONTEXT-AWARE MODELING SYSTEM THAT GIVEN ADEQUATE SEMANTICALLY-GROUNDED METADATA IS CAPABLE OF FINDING AND ASSESSING THE SUITABILITY OF CANDIDATE DATA SETS FOR USE WITH A PARTICULAR MODEL AND AUTOMATICALLY ESTABLISHING LINKAGES BETWEEN DATA SOURCES AND MODEL COMPONENTS PERFORMING A VARIETY OF MEDIATION AND PRE-PROCESSING TASKS TO INTEGRATE HETEROGENEOUS DATA SETS. THE AMP PROJECT AIMS TO USE MACHINE LEARNING TECHNIQUES TO AUTO-GENERATE SEMANTICALLY CONSISTENT VARIABLE-LEVEL METADATA RECORDS FOR NASA DATA PRODUCTS AND IN COLLABORATION WITH THE ARIES DEVELOPER AND USER COMMUNITIES DEMONSTRATE THEIR VALUE IN SUPPORTING SCIENTIFIC RESEARCH. IN SO DOING WE HOPE TO ACHIEVE SEVERAL OBJECTIVES: ADDRESS USABILITY AND SCALABILITY ISSUES FOR DATA PROVIDERS AND METADATA CURATORS IN CONNECTION WITH TOOLS FOR GENERATING ROBUST VARIABLE-LEVEL METADATA RECORDS IMPROVE THE SEMANTIC INTEROPERABILITY OF TARGET NASA DATA PRODUCTS BY LINKING CONCEPTS IN AMP-GENERATED METADATA RECORDS TO TERMS FROM WELL-ESTABLISHED EXTERNAL VOCABULARIES SUCH AS THE ENVIRONMENT ONTOLOGY (ENVO) THEREBY TAKING ADVANTAGE OF EXISTING TERM MAPPINGS BETWEEN ENVO AND ONTOLOGIES DEVELOPED BY OTHER COMMUNITIES OF PRACTICE INCLUDING NASA S SEMANTIC WEB FOR EARTH AND ENVIRONMENT TECHNOLOGY (SWEET) ONTOLOGY DEMONSTRATE THE BENEFITS OF SEMANTICALLY INTEROPERABLE FAIR DATA ACROSS COMMUNITIES OF PRACTICE. AMP WILL PROVIDE A PLATFORM FOR AUTO-GENERATING ROBUST FAIR-PROMOTING SEMANTICALLY CONSISTENT METADATA RECORDS USING NEURAL NETS TO ASSIGN VARIABLES TO ONTOLOGICAL CLASSES IN THE AMP ONTOLOGY. OUR APPROACH RECOGNIZES THAT THERE IS A WEALTH OF INFORMATION CONTAINED WITHIN THE DATA ITSELF WHICH CAN BE EXPLOITED TO GENERATE ACCURATE AND CONSISTENT METADATA. WE WILL WORK WITH THE GODDARD SPACE FLIGHT CENTER S (GSFC) EARTH SCIENCE (GES) DATA AND INFORMATION SERVICES CENTER (DISC) WHICH WILL PROVIDE ACCESS TO DATA VIA ITS OPENDAP SERVERS FOR TRAINING AND TESTING THE AMP PIPELINE AND FOR USE BY THE ARIES PLATFORM. AMP ADVANCES THE ANALYTIC CENTER FRAMEWORK OBJECTIVES OF ALLOWING SEAMLESS INTEGRATION OF NEW AND USER-SUPPLIED COMPONENTS AND DATA INCREASING RESEARCH CAPABILITIES AND SPEED HANDLING LARGE VOLUMES OF DATA EFFICIENTLY AND PROVIDING NOVEL DATA DISCOVERY TOOLS.
$1,036,352Lingua Logica Llc · · FY2020 · National Aeronautics and Space Administration
Identifying Mechanisms of Dementia: Role for MRI in the Era of Molecular Imaging
$1,034,999Clifford Jack · Mayo Clinic Rochester · R01 · FY2010 · AG
Scalable Robot Validation and Data Creation with Compositional Generative Simulation
$1,034,949Danfei Xu · Georgia Tech Research Corporation · · FY2024 · CSE
Chronic Kidney Disease in Children (CKiD V)
$1,034,674Bradley Alan Warady · Children'S Mercy Hosp (Kansas City, Mo) · U01 · FY2025 · DK
Functional and Structural Optical Brain Imaging
$1,034,342Amir H Gandjbakhche · Eunice Kennedy Shriver National Institute Of Child Health & Human Development · ZIA · FY2023 · HD
Quantitative Biophotonics for Tissue Characterization and Function
$1,034,342Amir H Gandjbakhche · Eunice Kennedy Shriver National Institute Of Child Health & Human Development · ZIA · FY2023 · HD
Knowledge Management Center for Illuminating the Druggable Genome
$1,034,327Tudor I Oprea · University Of New Mexico Health Scis Ctr · U24 · FY2020 · CA
SBE-UKRI: Contextually and probabilistically weighted auditory selective attention: from neurons to networks
$1,034,150Lori L Holt · Carnegie Mellon University · · FY2022 · SBE
Training in Biomedical Informatics at Columbia University
$1,033,704Noemie Elhadad · Columbia University Health Sciences · T15 · FY2025 · LM
Preeclampsia and the Brain: Small vessel disease and cognitive function in early midlife
$1,033,659Janet M Catov · Magee-Women'S Res Inst And Foundation · R01 · FY2023 · AG
MRI: Acquisition of a Hybrid Real-Time Simulator for Real-Time Power Grid Simulations
$1,033,396Dongliang Duan · University Of Wyoming · · FY2018 · ENG
Cognitive Assessment and Neuroimaging (CAN) Core E
$1,033,296Nan-Kuei Chen · University Of Arizona · U19 · FY2023 · AG
Comprehensive characterization of variants underlying heart and blood diseases with CRISPR base editing
$1,033,149Luca Pinello · Massachusetts General Hospital · UM1 · FY2021 · HG
Core Grant for Vision Research
$1,032,489Patricia Ann D'amore · Schepens Eye Research Institute · P30 · FY2022 · EY
Proteogenomic Predictors of Recurrence in Non-small Cell Lung Cancer
$1,032,286Ramaswamy Govindan · Washington University · U01 · FY2023 · CA
Spatial functional genomics to identify regulators of the tumor microenvironment and cancer immunity
$1,032,278Brian D Brown · Icahn School Of Medicine At Mount Sinai · U01 · FY2023 · CA
Building predictive algorithms to identify resilience and resistance to Alzheimer's disease
$1,032,210Rachel Frances Buckley · Massachusetts General Hospital · R01 · FY2024 · AG
REMOTE SENSING OBSERVATIONS OF MARINE ZOOPLANKTON HAVE GREAT POTENTIAL FOR ENHANCING GLOBAL CLIMATE MODELS AND ECOSYSTEM-BASED MANAGEMENT OF OCEAN RESOURCES. AS THE CRUCIAL LINK BETWEEN PRIMARY PRODUCTION AND UPPER TROPHIC LEVELS (SUCH AS FISH) ZOOPLANKTON GRAZING AND BIOMASS INFLUENCE BOTH CARBON EXPORT AND FISHERIES PRODUCTION. THE IMPORTANCE OF MESOSCALE FEATURES SUCH AS FRONTS AND EDDIES ON ZOOPLANKTON DYNAMICS ARE WELL ESTABLISHED BUT HIGH FREQUENCY LARGE-SCALE SAMPLING OF THESE FEATURES IS CURRENTLY ONLY POSSIBLE USING SATELLITES. NEXT GENERATION SENSORS WILL OBSERVE THE OCEAN AT HIGHER SPATIAL RESOLUTIONS INCLUDING PROXIES FOR SURFACE CURRENTS (E.G. SURFACE WATER OCEAN TOPOGRAPHY) AS WELL AS HIGHLY SPECTRALLY-RESOLVED MEASUREMENTS FOR DETECTING PHYTOPLANKTON FUNCTIONAL TYPES AND RATE PROCESSES (E.G. PRE-AEROSOL CLOUD OCEAN ECOSYSTEM). TO TRANSLATE THESE FINE-SCALE MEASUREMENTS OF OCEAN PHYSICS AND PHYTOPLANKTON INTO FISHERIES RELEVANT QUANTITIES WE MUST UNDERSTAND THEIR EFFECTS ON ZOOPLANKTON. OUR GOAL IS TO USE SPACE-BASED HIGH-RESOLUTION OBSERVATIONS TO PREDICT ZOOPLANKTON AND GRAZING IN RELATION TO MESOSCALE EDDY FEATURES. A CORNERSTONE OF OUR STRATEGY WILL BE LINKING AN EXISTING COUPLED OCEAN ECOSYSTEM MODEL WITH EXPLICIT MICROZOOPLANKTON TO A LAGRANGIAN INDIVIDUAL-BASED MODEL OF TWO FORMS OF MESOZOOPLANKTON (EACH WITH FIVE STAGES AND STAGE-SPECIFIC SIZE BEHAVIOR GROWTH AND GRAZING PROPERTIES). WE WILL USE THIS MODEL FOR DEVELOPING BOTH EULERIAN AND LAGRANGIAN ALGORITHMS THAT RELATE SURFACE OCEAN FIELDS IN THE MODEL RESULTS (SUCH AS: CURRENTS TEMPERATURE SIZE-SPECIFIC PHYTOPLANKTON CARBON BIOMASS AND PRODUCTION RATES) TO MICROZOOPLANKTON AND MESOZOOPLANKTON ABUNDANCE AND GRAZING. THE COUPLED MODELS WILL SERVE AS A KNOWN SYSTEM FOR UNDERSTANDING ERROR PROPAGATION. THE ALGORITHMS WILL THEN BE APPLIED TO SATELLITE-DERIVED FIELDS (E.G. PRIMARY PRODUCTIVITY SIZE-SPECIFIC PHYTOPLANKTON CARBON BIOMASS NET CHANGE IN PHYTOPLANKTON ABUNDANCE TEMPERATURE THERMOCLINE OR NUTRICLINE DEPTH) TO PREDICT MICRO AND MESOZOOPLANKTON ABUNDANCE. BECAUSE ZOOPLANKTON ALGORITHM DEVELOPMENT IS A CHALLENGING PROBLEM WE PROPOSE TO EVALUATE THREE COMPLEMENTARY ALGORITHM APPROACHES IN A SHARED QUANTITATIVE FRAMEWORK: EMPIRICAL ALGORITHMS BASED ON MACHINE LEARNING AND STATISTICAL INFERENCE MECHANISTIC ALGORITHMS BASED ON EQUATIONS DESCRIBING GROWTH AND GRAZING OF PHYTOPLANKTON AND THEORETICAL ALGORITHMS BASED ON THE FLOW OF ENERGY FROM ONE TROPHIC LEVEL TO THE NEXT USING ALLOMETRIC SCALING. WE WILL THEN VALIDATE THE SPACE-BASED ESTIMATES OF ZOOPLANKTON FIELDS IN TWO TEST REGIONS THE GULF OF MEXICO AND THE CALIFORNIA CURRENT SELECTED BASED ON THE AVAILABILITY OF EXTENSIVE EXISTING ZOOPLANKTON DATABASES. WE WILL QUANTITATIVELY EVALUATE THE SKILL OF THE SATELLITE-DERIVED ZOOPLANKTON FIELDS IN RELATION TO OBSERVATIONS OF MICRO- AND MESO-ZOOPLANKTON AND EDDY VARIABILITY. THE REALIZED ZOOPLANKTON DISTRIBUTIONS WILL BE USED TO ANSWER THE QUESTION; HOW DO FRONTS AND EDDIES IN THE OCEAN INFLUENCE TROPHIC TRANSFER OF ENERGY UP THE FOOD WEB INTO ZOOPLANKTON? LARVAL FISH OFTEN AGGREGATE IN FRONTS AND EDDIES INDICATING THAT TROPHIC LEVELS ABOVE ZOOPLANKTON RESPOND TO THE EDDYING ENVIRONMENT. WE HYPOTHESIZE THAT GRAZING AND TROPHIC TRANSFER BY ZOOPLANKTON WILL BE GREATER IN AN EDDYING OCEAN WHEN COMPARED TO LARGE SPATIAL AND TEMPORAL AVERAGES OF THESE PARAMETERS. THIS PROPOSED EFFORT ADVANCES NASA RESEARCH OBJECTIVES THROUGH IMPROVING SPACE-BASED PREDICTION OF OCEAN ECOSYSTEMS THAT CAN BE APPLIED TO ECOSYSTEM BASED MANAGEMENT OF THE OCEANS AT SCALES RELEVANT TO EMERGING TECHNOLOGIES AND PLANNED NASA MISSIONS.
$1,032,195University Of Maryland Center For Environmental Science · · FY2020 · National Aeronautics and Space Administration
NCANDA: DATA ANALYSIS RESOURCE
$1,032,001Adolf Pfefferbaum · Sri International · U24 · FY2020 · AA
BIGDATA: F: Random and Adaptive Projections for Scalable Optimization and Learning
$1,032,000Clayton Scott · Regents Of The University Of Michigan - Ann Arbor · · FY2019 · CSE
LEAP-HI: Hybrid Intelligence for Design: Bridging Human and Machine Intelligences for Collaborative Design of Engineering Systems and Infrastructure
$1,031,926Maria C Yang · Massachusetts Institute Of Technology · · FY2019 · ENG