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48,453 grants matching machine learning

KnowEng, a Scalable Knowledge Engine for Large-Scale Genomic Data-OVERALL

$3,640,781
Jiawei Han · University Of Illinois At Urbana-Champaign · U54 · FY2017 · GM

Prostate Cancer Imaging

$3,640,096
Peter L. Choyke · Division Of Basic Sciences - Nci · ZIA · FY2020 · CA

National Center: Multi-Scale Study of Cellular Networks(RMI)

$3,638,557
Andrea Califano · Columbia University Health Sciences · U54 · FY2007 · CA

Collaborative Research: Facility: Next Generation Interoperable Data Infrastructure for Geoscience Sample Data

$3,637,233
Kerstin A Lehnert · Columbia University · · FY2022 · GEO

The Psychiatric Cell Map Initiative: Connecting Genomics, Subcellular Networks, and Higher Order Phenotypes

$3,628,784
Nevan J Krogan · University Of California, San Francisco · U01 · FY2020 · MH

ITR: Language, Learning, and Modeling Biological Sequences

$3,625,503
Aravind K Joshi · University Of Pennsylvania · · FY2002 · CSE

Thalamus in the middle: computations in multi-regional neural circuits

$3,611,067
Karel Svoboda · Allen Institute · U19 · FY2024 · NS

Institute for Clinical and Translational Science

$3,609,157
Eric J Vilain · University Of California-Irvine · UL1 · FY2022 · TR

East Africa International Epidemiology Database to evaluate AIDS (IeDEA) Regional Consortium

$3,607,664
Kara Kay Wools-Kaloustian · Indiana University Indianapolis · U01 · FY2022 · AI

The Psychiatric Cell Map Initiative: Connecting Genomics, Subcellular Networks, and Higher Order Phenotypes

$3,605,729
Nevan J Krogan · University Of California, San Francisco · U01 · FY2019 · MH

Institute for Clinical and Translational Science

$3,604,234
Dan M Cooper · University Of California-Irvine · UL1 · FY2020 · TR

Institute for Clinical and Translational Science

$3,595,680
Dan M Cooper · University Of California-Irvine · UL1 · FY2019 · TR

National Center: Multi-Scale Study of Cellular Networks(RMI)

$3,570,137
Andrea Califano · Columbia University Health Sciences · U54 · FY2008 · CA

Collaborative Research: Variability-Aware Software for Efficient Computing with Nanoscale Devices

$3,569,996
Mani B Srivastava · University Of California-Los Angeles · · FY2010 · CSE

Development and validation of a porcine model of spinal cord injury-induced neuropathic pain

$3,560,956
Candace L Floyd · Emory University · RF1 · FY2023 · NS

Functional MRI Core Facility

$3,554,064
Peter Bandettini · National Institute Of Mental Health · ZIC · FY2019 · MH

Repository Core

$3,551,716
Lee-Way Jin · University Of California At Davis · U19 · FY2025 · NS

Accessible technologies for high-throughput, whole-brain reconstructions of molecularly characterized mammalian neurons

$3,550,431
Ulrich Mueller · Johns Hopkins University · RF1 · FY2019 · MH

THIS RESEARCH PROJECT IS DESIGNED TO EVALUATE THE IMPACTS OF OFFSHORE WIND DEVELOPMENT ON COMMERCIAL FISH SPECIES AND BENTHIC HABITATS AND COMMUNITIES USING A SUITE OF STATE-OF-THE-ART, NON-LETHAL SURVEY METHODS INCLUDING AN OPEN COD-END VIDEO TRAWL, A TOWED OFF-BOTTOM OPTICAL SURVEY VEHICLE, AND ANCHORED AND ROPELESS STATIONARY CAMERA SYSTEMS. SURVEYS WILL BE CONDUCTED BEFORE, DURING, AND AFTER WIND FARM CONSTRUCTION TO PROVIDE DATA ON CHANGES IN COMMERCIAL FISH AND MARINE INVERTEBRATE ABUNDANCE AND DISTRIBUTION, AND THE RELATIONSHIP OF BOTH TO HABITAT CHANGES, THE PRESENCE OF NEW STRUCTURES (TURBINE BASES), AND CHANGING UNDERWATER NOISE LEVELS. THROUGH USE OF MULTIPLE NON-LETHAL OPTICAL SURVEY METHODS, THIS PROJECT WILL PROVIDE A HOLISTIC VIEW OF THE HABITATS AND COMMUNITIES THAT MAY BE IMPACTED BY OFFSHORE WIND DEVELOPMENT OFF MASSACHUSETTS AND RHODE ISLAND. IN ADDITION, AUTOMATED DETECTORS FOR TWENTY-FOUR FISH AND INVERTEBRATE SPECIES, CHOSEN BECAUSE OF THEIR ECONOMIC IMPORTANCE TO THE REGION, WILL BE DEVELOPED USING THE IMAGERY COLLECTED DURING THE SURVEYS. PROJECT EFFORTS WILL RESULT IN A NEW METHODOLOGICAL FRAMEWORK FOR MONITORING MARINE SPECIES IN WIND FARMS USING OPTICAL SURVEYS, INCLUDING PREFERRED SURVEY DESIGNS, FREELY AVAILABLE AUTOMATED DETECTORS AND IMAGE SETS FOR TRAINING NEW MACHINE LEARNING ALGORITHMS, AND SCHEMATICS FOR ANY NEW GEAR DESIGNS.

$3,543,697
Coonamessett Farm Foundation Inc · · FY2022 · Department of Energy

FODAVA-Lead: Dimension Reduction and Data Reduction: Foundations for Visualization

$3,539,333
Haesun Park · Georgia Tech Research Corporation · · FY2008 · CSE

ITR: Interacting with the Visual World: Capturing, Understanding, and Predicting Appearance

$3,525,947
Shree K Nayar · Columbia University · · FY2000 · CSE

Collaborative Research: Frameworks: Ghub as a Community-Driven Data-Model Framework for Ice-Sheet Science

$3,522,878
Jason P Briner · Suny At Buffalo · · FY2020 · CSE

Thalamus in the middle: computations in multi-regional neural circuits

$3,519,902
Karel Svoboda · Allen Institute · U19 · FY2025 · NS

Translational Research Core

$3,518,272
Colin Osborne · University Of Texas Med Br Galveston · U19 · FY2023 · AI

Functional MRI Method Development

$3,514,361
Peter Bandettini · National Institute Of Mental Health · ZIA · FY2024 · MH