Government Contract Award Search - Machine Learning | Federal Compass

Government Contract Award Search - Machine Learning

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75N91021P00995 - ACQUISITION OF ONE (1) LEICA STELLARIS 8 CONFOCAL AND CRS (CARS AND SRS) MICROSCOPE
Purchase Order - 334516 Analytical Laboratory Instrument Manufacturing
Contractor
Danaher Corporation (LEICA MICROSYSTEMS INC.)
Contracting Agency/Office
Health and Human Services»National Institutes of Health»National Cancer Institute
Effective date
09/27/2021
Obligated Amount
$945.4k
75A50121C00062 - AIDAR HEALTH - AIDI: A MULTISENSOR-BASED MACHINE LEARNING TECHNOLOGY FOR REAL-TIME AUTOMATED DETECTION OF COVID-19 DECOMPENSATION WILL PROGRESS IN WORK SEGMENTS WITH KEY DELIVERABLES BEING DUE DURING THE BASE PERIOD OF PERFORMANCE OF THE CONTRACT; T
Definitive Contract - 325413 In-Vitro Diagnostic Substance Manufacturing
Contractor
AIDAR HEALTH, INC
Contracting Agency/Office
Health and Human Services»Office of the Secretary»Office of the Assistant Secretary for Preparedness and Response»Office of Acquisition Management, Contracts, & Grants
Effective date
09/24/2021
Obligated Amount
$529.9k
36C24E21P0189 - MATLAB WITH TOOLBOXES
Purchase Order - 511210 Software Publishers
Contractor
CITIBANK USA (G.C.MICRO CORPORATION)
Contracting Agency/Office
Veterans Affairs»Veterans Health Administration»Office of Procurement and Logistics»OPL Office of Procurement
Effective date
09/23/2021
Obligated Amount
$111.8k
75N91021C00040 - SBIR TOPIC 425 PHASE I: POSTURE ANALYSIS THROUGH MACHINE LEARNING (PATHML)
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
SENTIMETRIX, INC.
Contracting Agency/Office
Health and Human Services»National Institutes of Health»National Cancer Institute
Effective date
09/21/2021
Obligated Amount
$399.9k
80NSSC21M0322 - OUR GOAL IS TO LEVERAGE NEW DATA SCIENCE/MACHINE LEARNING EXTENSIONS MADE POSSIBLE BY CONTEMPORARY COMPUTATIONAL RESOURCES TO DEVELOP NEW DEEP LEARNING ALGORITHMS/TECHNOLOGY
Cooperative Agreement
Contractor
WEST VA U RESEARCH COR (WEST VIRGINIA UNIVERSITY RESEARCH CORPORATION)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
09/15/2021
Obligated Amount
$750k
1232SA21C0014 - USDA NAL COGITO SOFTWARE SUPPORT SERVICES
Definitive Contract - 511210 Software Publishers
Contractor
EXPERT SYSTEM ENTERPRISE CORP.
Contracting Agency/Office
Agriculture»Agricultural Marketing Service
Effective date
09/14/2021
Obligated Amount
$102.5k
1333ND21PNB640550 - 2 INCUBATOR SHAKERS
Purchase Order - 334516 Analytical Laboratory Instrument Manufacturing
Contractor
FRANKLIN YOUNG INTERNATIONAL, INCORPORATED
Contracting Agency/Office
Commerce»National Institute of Standards and Technology»Management Resources»Office of Acquisition and Agreements Management
Effective date
09/13/2021
Obligated Amount
$19.1k
80NSSC21K1813 - INTERPRETABLE MACHINE LEARNING FOR HIGH-SPEED, HIGH-FIDELITY GEOS-CHEM MODEL SIMULATIONS WITH UNCERTAINTY QUANTIFICATION
Grant for Research
Contractor
BOARD OF TRUSTEES, U. OF IL (UNIVERSITY OF ILLINOIS)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
09/10/2021
Obligated Amount
$161.2k
75N95021C00014 - A MICROFLUIDIC AND MACHINE LEARNING-ENABLED SMARTLAB FOR AUTONOMOUS REMOTE EXECUTION AND ITERATION OF MULTISCALE LIVE CELL ASSAYS FOR DRUG DISCOVERY
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
CAIRN BIOSCIENCES, INC.
Contracting Agency/Office
Health and Human Services»National Institutes of Health»National Institute on Drug Abuse
Effective date
09/09/2021
Obligated Amount
$331.5k
80NSSC21K1371 - A KEY QUESTION CENTRAL TO THIS RESEARCH PROPOSAL IS, CAN WE USE SYNTHETIC UV AND EUV IRRADIANCE DATA, PHYSICS-BASED MODELS, AND MACHINE LEARNING
Grant for Research
Contractor
S E T I INSTITUTE (SETI INSTITUTE)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
09/07/2021
Obligated Amount
$200k
140G0321P0327 - SCIENTIFIC DEVELOPMENT SERVICES/MACHINE LEARNING DEVELOPMENT
Purchase Order - 541620 Environmental Consulting Services
Contractor
CONSERVATION METRICS, INC.
Contracting Agency/Office
Interior»U.S. Geological Survey»USGS Office of Administration»USGS Office of Acquisition and Grants
Effective date
09/02/2021
Obligated Amount
$24.8k
75N94021P00796 - NIDDK - DOUBLE-PRECISION GENERAL-PURPOSE GRAPHICS PROCESSING UNIT (GPGPU) SERVER
Purchase Order - 334118 Computer Terminal and Other Computer Peripheral Equipment Manufacturing
Contractor
ASPEN SYSTEMS INC (ASPEN SYSTEMS, INC.)
Contracting Agency/Office
Health and Human Services»National Institutes of Health»Eunice Kennedy Shriver National Institute of Child Health and Human Development
Effective date
08/27/2021
Obligated Amount
$35.7k
80NSSC21K1149 - THE OVERARCHING GOAL OF THIS PROPOSAL IS TO DEVELOP AN AUTOMATED SHORELINE EXTRACTION TECHNIQUE USING SMALLSAT IMAGERY THAT WILL ENABLE MAPPING OF HIGH-RESOLUTION SHORELINE POSITIONS ACROSS REGIONAL-TO-CONTINENTAL SCALES. THIS NOVEL TECHNIQUE WILL FACILITATE COASTAL MONITORING AT SPATIAL RESOLUTIONS COMPARABLE TO AIRBORNE SURVEYS BUT AT THE DESIRED TEMPORAL FREQUENCY OF A SATELLITE CONSTELLATION. THIS ADVANTAGEOUS COMBINATION WILL SIGNIFICANTLY ENHANCE OUR ABILITY TO GATHER TIMELY INFORMATION ABOUT SHORELINE CHANGES IN THE AFTERMATH OF MAJOR STORMS AND DETECT ONGOING SMALL-SCALE CHANGES IN RESPONSE TO SEA LEVEL RISE, AMONG OTHER APPLICATIONS. OBJECTIVES: THE CENTRAL OBJECTIVES OF THIS PROPOSAL INCLUDE (1) DEVELOPING AN AUTOMATED SHORELINE MAPPING METHODOLOGY USING EMERGING IMAGE ANALYSIS TECHNIQUES (MACHINE LEARNING) AND GEOSPATIAL PLATFORMS (GOOGLE EARTH ENGINE), (2) ASSESSING THE ACCURACY OF SHORELINE POSITIONS DERIVED FROM SMALLSAT IMAGERY BY COMPARISON TO MEAN HIGH WATER SHORELINE POSITIONS INTERPRETED FROM UNMANNED AIRCRAFT SYSTEM (UAS) IMAGERY, AND (3) IDENTIFYING THE SMALLSAT DATA PRODUCT BEST SUITED FOR COASTAL MONITORING BASED ON SHORELINE ACCURACY AND SURVEY FREQUENCY. METHODS: THE FIRST COMPONENT INVOLVES ACQUISITION OF UAS IMAGERY AT THREE COASTAL STUDY AREAS IN SOUTHERN CALIFORNIA THAT WILL SERVE AS VALIDATION DATASETS FOR THE SMALLSAT-DERIVED SHORELINE POSITIONS. THE SECOND COMPONENT INVOLVES THE GENERATION OF UAS PHOTOGRAMMETRY PRODUCTS AND VALIDATION DATA. THE UAS IMAGERY WILL BE USED TO GENERATE GEOREFERENCED DIGITAL SURFACE MODELS (DSM), FROM WHICH MEAN HIGH WATER (MHW) SHORELINE POSITIONS WILL BE INTERPRETED AND USED TO ASSESS THE ACCURACY OF SHORELINES INTERPRETED FROM SMALLSAT IMAGERY. ULTRA-HIGH RESOLUTION ORTHOPHOTO MOSAICS WILL ALSO BE GENERATED FROM THE UAS IMAGERY, WHICH WILL BE USED TO ASSESS THE PERFORMANCE OF THE MACHINE LEARNING CLASSIFIER USED TO AUTOMATICALLY INTERPRET THE SHORELINE POSITION. THE THIRD AND FINAL COMPONENT IS DEVELOPING THE AUTOMATED SHORELINE EXTRACTION TECHNIQUE USING MACHINE LEARNING CLASSIFICATION. ALL OF THE IMAGE PROCESSING AND ANALYSIS TASKS WILL BE PERFORMED IN GOOGLE EARTH ENGINE AND SCRIPTS WILL BE DISSEMINATED TO PUBLIC REPOSITORIES TO ENCOURAGE FUTURE USE. FINALLY, THE ACCURACY OF SHORELINES DERIVED FROM THE VARIOUS SMALLSAT PRODUCTS WILL BE ASSESSED BY COMPARISON TO THE UAS MHW SHORELINE POSITION. SIGNIFICANCE TO CSDAP: THIS PROPOSAL FALLS WITHIN THE GENERAL SCOPE OF A.42 AND ADDRESSES STRATEGIC GOAL 1 OF NASA S 2018 STRATEGIC PLAN. THE MOST IMPORTANT OUTCOME OF THIS PROPOSAL WILL BE ITS CONTRIBUTION AS A SOCIETAL BENEFIT AND APPLICATION FOR NATURAL HAZARD PREPAREDNESS AND RESPONSE. SANDY COASTLINES ARE BECOMING INCREASINGLY SUSCEPTIBLE TO GLOBAL SEA-LEVEL RISE AND IT'S PREDICTED THAT HALF OF THE WORLD'S BEACHES COULD BE COMPLETELY ERODED BY 2100. CALIFORNIA S DUNE-BACKED SHORELINES ARE ESPECIALLY VULNERABLE, AND THE U.S. GEOLOGICAL SURVEY HAS ESTABLISHED A COASTAL CHANGE HAZARDS PORTAL TO PROVIDE SCIENTIFICALLY CREDIBLE DATA TO HELP MANAGE THESE COASTAL HAZARDS. THE MOST RECENT STATEWIDE SHORELINE MAPPED BY THE USGS WAS IN 2002 DUE TO THE HIGH ACQUISITION COSTS OF AIRBORNE SURVEYING AND AS SUCH, THERE IS A CRITICAL NEED FOR A SHORELINE MAPPING TECHNIQUE THAT TAKES ADVANTAGE OF MODERN SATELLITE TECHNOLOGY AND DATA ANALYSIS TECHNIQUES. THE PRODUCTS OF THIS RESEARCH WILL SIGNIFICANTLY ASSIST COASTAL LAND USE MANAGERS AND OTHER DECISION MAKERS WITH IDENTIFYING EROSIONAL HOT SPOTS THAT SHOULD BE TARGETED FOR RESTORATION EFFORTS. THE PROPOSED WORK WILL ALSO ADVANCE OUR UNDERSTANDING OF EARTH SYSTEM SCIENCE AS IT WILL PROVIDE A GROUNDBREAKING TECHNIQUE FOR ASSESSING GLOBAL COASTAL CHANGE. HAVING THIS NEWFOUND CAPABILITY TO DETECT HIGH SPATIAL RESOLUTION (<5 M) SHORELINE POSITION CHANGES ACROSS CONTINENTAL-SCALE STUDY AREAS WILL FACILITATE INVESTIGATIONS INTO THE RELATIONSHIP BETWEEN THE EARTH S CLIMATE SYSTEM AND GEOSPHERE
Grant for Research
Contractor
CAL STATE L.A. UNIVERSITY AUXILIARY SERVICES, INC.
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
08/27/2021
Obligated Amount
$141.8k
80NSSC21M0222 - OBJECTIVES: WE PROPOSE TO EVALUATE THE PACKAGING STABILITY AND SUB-SYSTEM LEVEL RELIABILITY OF THE INTEGRATED PHOTONIC RECEIVERS, INCLUDING MACHINE LEARNING
Cooperative Agreement
Contractor
UNIV DELAWARE (UNIVERSITY OF DELAWARE)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
08/26/2021
Obligated Amount
$100k
80NSSC21K1496 - THE MAIN OUTPUT FROM THIS PROPOSAL WILL BE A NEW VEGETATION MODEL COUPLED WITH GISS-E, DATA ASSIMILATION AND MACHINE LEARNING ALGORITHMS FOR CRITICAL VEGETATION PROCESSES
Grant for Research
Contractor
Columbia University (TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK, THE)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
08/26/2021
Obligated Amount
$129.3k
80NSSC21K1003 - THE DYNAMIC MASS LOSS DUE TO ICE FLOW FROM THE ANTARCTIC ICE SHEET DIRECTLY INTO OCEANS IS ONE OF THE GREATEST SOURCES OF UNCERTAINTY IN PREDICTING SEA LEVEL RISE. OVER 80% OF THE ANTARCTIC ICE SHEET DRAINS INTO THE OCEAN THROUGH FLOATING ICE SHELVES, WHERE ALL MASS LOSS OCCURS DUE TO ROUGHLY EQUAL CONTRIBUTIONS FROM BASAL MELTING AND TABULAR CALVING. ICE SHELVES PROVIDE RESISTANCE TO THE FLOW OF UPSTREAM GROUNDED GLACIER ICE INTO THE OCEAN, SO ANY LOSS OF THIS RESISTANCE OR BUTTRESSING DUE TO CALVING COULD TRIGGER MARINE ICE CLIFF INSTABILITY AND INCREASE THE FLUX OF GROUNDED ICE FLOW INTO THE OCEAN, THEREBY CONTRIBUTING TO SEA LEVEL RISE. IMPORTANTLY, CALVING IS COMPLICATED BY ICE-OCEAN-ATMOSPHERIC COUPLINGS AND THE MAJOR CONCERN IS THAT FUTURE WARMER CLIMATES AND OCEANS COULD INCREASE CALVING RATES AND DESTABILIZE GLACIERS AND ICE SHELVES. THE PROJECT S OVERALL OBJECTIVE IS TO BETTER UNDERSTAND ICE SHELF AND GLACIER WEAKENING AND CALVING PROCESS, THE STABILITY OF FRACTURES, AND THE DAMAGE MECHANICS OF MARGINAL SHEAR ZONES UNDER DIFFERENT FLOW REGIMES. OUR HYPOTHESIS IS THAT OBSERVABLE FRACTURE FEATURES IN ICE ARE MANIFESTATIONS OF THERMO-MECHANICAL DAMAGE EVOLUTION IN SPACE AND TIME. TO TEST THIS HYPOTHESIS AND ADDRESS THE OBJECTIVE, WE WILL DEVELOP AN ADVANCED MODELING APPROACH THAT INTEGRATES MECHANICS-BASED COMPUTATION WITH REMOTELY SENSED DATA COMPILED USING STATISTICAL ANALYSIS AND MACHINE LEARNING. WE APPLY OUR MODELING APPROACH TO STUDY DAMAGE EVOLUTION IN LARSEN C AND CROSSON ICE SHELVES AND UNDERSTAND THE CONSEQUENCES FOR ITS UPSTREAM GLACIERS. WE PROPOSE TO USE ALTIMETRY DATA, INCLUDING ICESAT-2, ATM (AIRBORNE TOPOGRAPHIC MAPPER), AND LVIS (LAND VEGETATION, AND ICE SENSOR), AS WELL AS HIGH-RESOLUTION DEM DATA FROM THE REMA PROJECT TO MEASURE SURFACE ROUGHNESS TO MAP SHEAR ZONES AND RIFTS, DERIVE SPOT ESTIMATES OF CREVASSE DEPTHS, AND TO MEASURE M LANGE FREEBOARD IN RIFTS. WE WILL IMPLEMENT DEEP CONVOLUTIONAL NEURAL NETWORKS, A TYPE OF ADVANCED MACHINE LEARNING MODEL, TO AUTOMATE THE PROCESS OF RIFT AND CREVASSE DAMAGE IDENTIFICATION FROM SATELLITE-BASED IMAGES. BY COMBINING IDENTIFIED DAMAGE FEATURE INFORMATION WITH CREVASSE DEPTH DATA FROM ICESAT-2, WE WILL GENERATE USEFUL DATASETS OR DAMAGE-MAPS FOR CALIBRATION AND VALIDATION OF THE INVERSE AND FORWARD COMPUTATIONAL MODELS. SUBSEQUENTLY, WE WILL STUDY OF RIFT DYNAMICS TO BETTER UNDERSTAND THE FEEDBACK BETWEEN DAMAGE ACCUMULATION, RIFT PROPAGATION AND ICE RHEOLOGY. THE PROJECT ADDRESSES THE SOLICITATION S FOCUS TO UTILIZE REMOTE SENSING DATA TO IMPROVE OUR UNDERSTANDING OF ICE SHEET, ICE SHELF, AND GLACIER PROCESSES AND HOW THESE PROCESSES AFFECT ICE MASS BALANCE AND ULTIMATELY SEA LEVEL RISE AND ESTABLISH TIME SERIES OF ICE SHEET GEOPHYSICAL PARAMETERS AND INVESTIGATE TRENDS AND VARIABILITIES, AND THEIR DRIVERS TO AID MODEL PREDICTIONS. THE PROJECT BUILDS ON A DATABASE OF NASA ICEBRIDGE, ICESAT, AND ICESAT-2 OBSERVATIONS PREVIOUSLY CONSTRUCTED BY CO-I SMITH AND WILL UTILIZE A VARIETY OF SATELLITE AND AIRBORNE BASED MEASUREMENTS TO QUANTIFY THE LOCATION AND GEOMETRY OF RIFTS AND SURFACE CREVASSES. AS A MEMBER OF THE ICESAT-2 SCIENCE TEAM, CO-I SMITH WILL COLLABORATE WITH OTHER TEAM MEMBERS SO THAT OUR DATA AND CODE HELP ADDRESS THE NEEDS OF THE SCIENCE TEAM, INCLUDING QUANTIFICATION OF UNCERTAINTIES IN THE ATL06 AND ATL11 DATASETS IN REGIONS OF CREVASSING, AND WILL WORK WITH SCIENCE TEAM MEMBERS H. FRICKER AND B. LIPOVSKY TO INTEGRATE THEIR DATABASES OF RIFT LOCATIONS WITH OUR WORK. THE DATASETS AND PYTHON CODE PRODUCED BY THE PROJECT WILL BE SHARED OPENLY FOLLOWING FINDABLE, ACCESSIBLE, INTEROPERABLE, AND REUSABLE PRACTICES SO THAT THEY CAN BE USED BY THE ICESAT-2 SCIENCE TEAM AND THE BROADER COMMUNITY. WE WILL ALSO SHARE OUR IMPLEMENTATION OF THE COMPUTATIONAL AND MACHINE LEARNING MODELS BUILT USING OPEN SOURCE.
Grant for Research
Contractor
THE VANDERBILT UNIVERSITY (VANDERBILT UNIVERSITY, THE)
Contracting Agency/Office
National Aeronautics and Space Administration»Mission Support Directorate»NASA Shared Services Center
Effective date
08/25/2021
Obligated Amount
$56.6k
75D30121C12375 - FY21 BAA MINE HEALTH AND SAFETY BIG DATA ANALYSIS AND TEXT MINING BY MACHINE LEARNING ALGORITHMS
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
MICHIGAN TECHNOLOGICAL UNIV (MICHIGAN TECHNOLOGICAL UNIVERSITY)
Contracting Agency/Office
Health and Human Services»Centers for Disease Control and Prevention
Effective date
08/25/2021
Obligated Amount
$288.3k
75D30121C12206 - FY21 BAA MACHINE LEARNING ENHANCED PERCEPTION FOR AUTOMATED OR REMOTE ROOF BOLTING OPERATIONS IN UNDERGROUND MINING
Definitive Contract - 541715 Research and Development in the Physical, Engineering, and Life Sciences
Contractor
CO SCHOOL OF MINES BUILDING CO (TRUSTEES OF THE COLORADO SCHOOL OF MINES)
Contracting Agency/Office
Health and Human Services»Centers for Disease Control and Prevention
Effective date
08/19/2021
Obligated Amount
$604.7k
140R8121P0096 - MACHINE LEARNING SUBSEASONAL FOREACSTING
Purchase Order - 541990 All Other Professional, Scientific, and Technical Services
Contractor
University of California (UNIVERSITY OF CALIFORNIA SAN DIEGO)
Contracting Agency/Office
Interior»Bureau of Reclamation»Office of Policy, Administration and Budget»Mission Support Organization
Effective date
08/16/2021
Obligated Amount
$250k

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