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15,895 grants matching “artificial intelligence”
III: Small: Towards the Foundations of Training Deep Neural Networks: New Theory and Algorithms
$500,000Quanquan Gu · University Of California-Los Angeles · · FY2020 · CSE
Collaborative Research: CUE-T: Hidden Curricula-Addressing Unseen Challenges within Computer Science Education
$500,000Ricardo Eiris · Arizona State University · · FY2025 · CSE
Particle Astrophysics with The Super-Kamiokande Detector
$500,000Henry W Sobel · University Of California-Irvine · · FY2024 · MPS
CIF: Small: Computationally Efficient Second-Order Optimization Algorithms for Large-Scale Learning
$500,000Aryan Mokhtari · University Of Texas At Austin · · FY2020 · CSE
CAREER: Additive Manufacturing of Property-Certified Metamaterials
$500,000Chen Kan · University Of Texas At Arlington · · FY2025 · ENG
SBIR Phase II: Artificial Intelligence Tutoring and Assessment for Teacher Development
$500,000Benny G Johnson · Quantum Simulations Incorporated · · FY2008 · TIP
Collaborative Research: DMREF: OP: Multi-Scale Engineered Metamaterials Approaching Fundamental Limits of Linear and Nonlinear Susceptibilities
$500,000Owen D Miller · Yale University · · FY2025 · MPS
Advancing the Use of LLMs in Civic Contexts
$500,000Yanna Krupnikov · Regents Of The University Of Michigan - Ann Arbor · · FY2025 · SBE
HCC: Small: Responsible Robot and AI Literacy: Embodied Informal Learning on Ethical Robotics and AI through a Creative Afterschool Program
$500,000Myounghoon Jeon · Virginia Polytechnic Institute And State University · · FY2025 · CSE
CRI: CRD - Developing a Dark Web Collection and Infrastructure for Computational and Social Sciences
$500,000Hsinchun Chen · University Of Arizona · · FY2007 · CSE
CAREER: Bio-inspired design methods for distributed electromechanical actuators
$500,000Arijit Banerjee · University Of Illinois At Urbana-Champaign · · FY2020 · ENG
SHF: Medium: Collaborative Research: Bridging Automated Formal Reasoning and Continuous Optimization for Provably Safe Deep Learning
$500,000Swarat Chaudhuri · University Of Texas At Austin · · FY2020 · CSE
Equipment and Administrative Supplement- THE HARC CENTER 2.0: HIV ACCESSORY AND REGULATORY COMPLEXES
$500,000Nevan J Krogan · University Of California, San Francisco · U54 · FY2024 · AI
GOALI: Federated Interdependency Learning for Securing Distributed Manufacturing Systems
$500,000Nagi Gebraeel · Georgia Tech Research Corporation · · FY2025 · ENG
Machine Learning Serpentines
$500,000Renata M Wentzcovitch · Columbia University · · FY2025 · GEO
OAC Core: Small: Next-Generation Communication and I/O Middleware for HPC and Deep Learning with Smart NICs
$500,000Dhabaleswar K Panda · Ohio State University, The · · FY2020 · CSE
CAREER: The Morpho-Molecular Tissue Atlas: A Framework for the Generation and Comparative Profiling of Terabyte-Scale Tissue Phenotypes
$500,000David Mayerich · University Of Houston · · FY2020 · CSE
CAREER: Conflicting Traffic Streams with Mixed Traffic: Modeling and Control
$500,000Danjue Chen · University Of Massachusetts Lowell · · FY2020 · ENG
CAREER: Understanding the Inductive Biases in Modern Machine Learning
$500,000Raman Arora · Johns Hopkins University · · FY2020 · CSE
Collaborative Research: RI: Medium: Lie group representation learning for vision
$499,999Bruno A Olshausen · University Of California-Berkeley · · FY2023 · CSE
III: Small: Exploiting the Massive User Generated Utterances for Intent Mining under Scarce Annotations
$499,999Philip S Yu · University Of Illinois At Chicago · · FY2019 · CSE
CAREER: Data Valuation in the Wild: Theories, Algorithms, and Applications
$499,999Ruoxi Jia · Virginia Polytechnic Institute And State University · · FY2023 · CSE
SHF:Small:Intelligent Management of Hybrid Workloads for Extreme Scale Computing
$499,999Zhiling Lan · Illinois Institute Of Technology · · FY2021 · CSE
CPS: Small: Advanced Hyperdimensional and Symbolic Knowledge Transfer for Cyber-Physical Systems
$499,999Mohsen Imani · University Of California-Irvine · · FY2025 · ENG
COMPUTER VISION AND DATA-DRIVEN APPROACHES ARE USED TO ENHANCE EXISTING DNA AND GROWTH BEEF INDUSTRY DATASETS (>150,592 CATTLE WITH 50 MILLION DNA VARIANTS) WITH DENSE DATA FROM THREE-DIMENSIONAL (3D) CAMERAS (~5,000 COWS, ~7 MILLION POINTS PER COW) TO ANSWER 170-YEAR-OLD QUESTIONS IN ECOLOGY AND EVOLUTION (BERGMANN'S RULE). USING YIELD, ENVIRONMENTAL STRESS, AND PATHOGEN RESISTANCE, THE PROJECT CREATES DNA PREDICTIONS TO HELP FARMERS SELECT CATTLE THAT THRIVE IN THE ENVIRONMENT AT THEIR RANCH, ULTIMATELY IMPROVING THE ENVIRONMENTAL EFFICIENCY OF BEEF PRODUCTION. IDENTIFYING ADAPTED, ENERGY EFFICIENT CATTLE ALLOWS FAMILY FARMS AND RANCHES ACROSS THE USA TO BE MORE PROFITABLE AND IMPROVES ANIMAL WELFARE, AN IMPORTANT SOCIETAL ISSUE. IN OBJECTIVE 1, 3D COMPUTER VISION IS USED TO MEASURE THE SURFACE AREA AND VOLUME OF CATTLE TO TEST IF SURFACE-AREA-TO-VOLUME RATIO (SA:V) INFLUENCES GENETICS FOR GROWTH UNDER COLD STRESS OR GROWTH UNDER HIGH FEED RESOURCES. LARGER ANIMALS HAVE A LOWER SA:V, ALLOWING THEM TO RETAIN HEAT MORE EFFICIENTLY IN WINTER. ONE HYPOTHESIS SUGGESTS LARGER ANIMALS LIVE AT HIGHER LATITUDES (FURTHER NORTH IN THIS STUDY) BECAUSE THEY USE THE LOWER SA:V TO BETTER DEAL WITH COLD STRESS. AN ALTERNATIVE HYPOTHESIS SUGGESTS THAT ANIMALS ARE LARGER AT HIGHER LATITUDES BECAUSE MORE FOOD IS AVAILABLE WHEN THE ANIMALS ARE GROWING. THIS RESEARCH CREATES TWO DNA PREDICTIONS, ONE TAILORED TO COLD STRESS AND THE SECOND TAILORED TO HIGH FEED RESOURCES. GENETIC MERITS OF ANIMALS WITHIN EACH OF THESE PRODUCTION CONTEXTS WILL BE DIRECTLY COMPARED TO THEIR SA:V RATIOS. IN OBJECTIVE 2, THIS PROJECT TESTS THE HYPOTHESIS THAT RANDOM MODELS VERSUS MACHINE LEARNING METHODS BETTER PREDICT VARIOUS CATTLE TRAITS (FEED INTAKE, FAT RESERVES, BODY WEIGHT, ETC.) USING PROCESSED 3D POINT CLOUDS AND ENVIRONMENTAL DATA. THE PROJECT USES RANDOM MODELS TYPICALLY USED IN DNA PREDICTIONS, BUT RATHER THAN DNA VARIANTS AS THE PREDICTORS, THE ANALYSIS USES THE LARGE 3D CAMERA DATA AS PREDICTORS.BESIDES ADVANCES IN COMPUTER VISION ALGORITHMS (E.G. STRUCTURE FROM MOTION) AND NEW APPROACHES TO APPLY DATA-DRIVEN MACHINE LEARNING TO AGRICULTURE PROBLEMS, THIS PROJECT PROVIDES A DEFINITIVE TEST OF JAMES'S RULE (INTRASPECIFIC BERGMANN'S RULE) THAT ANIMAL SIZE INCREASES WITH LATITUDE DUE TO AN INCREASED ABILITY TO MAINTAIN BODY TEMPERATURE WITH A SMALLER SA:V RATIO. THIS IS CONTRASTED WITH GEIST'S RULE THAT BODY SIZE INCREASES DUE TO ADVANTAGES OF LARGER ANIMALS TO UTILIZE SEASONAL BURST OF FOOD AT HIGHER LATITUDES. EXPLICITLY COMPARING DIRECT SA:V MEASUREMENTS WITH PRECISE CLIMATE-BY-GROWTH AND RESOURCE-BY-GROWTH PREDICTIONS, THIS PROJECT WILL ANSWER WHICH FORCE DRIVES EVOLUTION IN BODY SIZE ACROSS LATITUDES. THIS WILL BE THE FIRST DEFINITIVE TEST OF BERGMANN'S RULE. BY COMPARING RANDOM MODELS VERSUS DEEP LEARNING TO PREDICT A VARIETY OF BEEF COW PRODUCTION TRAITS USING 3D CAMERA DATA, THIS PROJECT INDICATES WHETHER RANDOM MODELS CAN REPLACE OR CLARIFY ARTIFICIAL INTELLIGENCE IN MANY CONTEXTS. THIS PROJECT CREATES NEW DATA RECORDING AND SELECTION TOOLS BASED ON INTERNET OF THINGS AND 3D COMPUTER VISION ALGORITHMS TO ADDRESS THE PRESSING NEED TO MEASURE AND IMPROVE EFFICIENCY IN PASTURE, COW-CALF PRODUCTION (A $64 BILLION INDUSTRY). THIS ENABLES CONVENIENT AND AFFORDABLE MEASUREMENT OF BEEF COW EFFICIENCY (A VITAL ASPECT OF BEEF PRODUCTION THAT IS NOT CURRENTLY PREDICTED).PARTNERING WITH THE UNIVERSITY OF MISSOURI SCHOOL OF JOURNALISM STRATEGIC COMMUNICATION CAPSTONE COURSE PROVIDES STRATEGIC PLANS TO EDUCATE A VARIETY OF AUDIENCES ON THE TRUE ENVIRONMENTAL IMPACT OF CATTLE AND HOW TECHNOLOGY ADOPTION DECREASES THIS IMPACT. THE THREE-PRONGED APPROACH 1) EDUCATES LAY AUDIENCES 2) DELIVERS ONLINE EDUCATIONAL OUTREACH TO FARMERS/RANCHERS AND 3) PROVIDES TRADITIONAL EXTENSION PROGRAMMING TO FARMERS/RANCHERS THROUGH REGIONAL AND NATIONAL IN-PERSON PRESENTATIONS. THE FIRST PRONG WILL REACH A NATIONAL AUDIENCE THROUGH CABLE TELEVISION. THE SECOND AND THIRD PRONG WILL REACH APPROXIMATELY 7,000 PEOPLE PER YEAR. WITH THE NATIONAL CENTER FOR APPLIED REPRODUCTION AND GENOMICS, A DATA SCIENCE BEST PRACTICES MODULE IS CREATED TO GIVE VETERINARY AND GRADUATE STUDENTS A PRIMER ON DATA-DRIVEN AGRICULTURE. PARTNERSHIPS WITH INDUSTRY CREATE APPLIED GENETIC EVALUATIONS TO PREDICT GENETIC, ENVIRONMENT, AND MANAGEMENT INTERACTIONS, WHICH ALLOW PRECISION SELECTION NOT CURRENTLY POSSIBLE WITH ONE-SIZE-FITS-ALL NATIONAL EVALUATIONS. THE COW SCANNING SYSTEM WILL BE COMMERCIALIZED AND ADDED ONTO EXISTING COMMERCIAL PLATFORMS THAT COLLECT FEED AND WATER INTAKE. THE 3D DATA IS USED TO PREDICT COW EFFICIENCY. COLLECTIVELY, THESE IMPACTS IMPROVE THE FINANCIAL SECURITY OF RURAL FARMERS.
$499,999University Of Missouri System · · FY2021 · National Institute of Food and Agriculture