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15,895 grants matching “artificial intelligence”
CAREER: Learning to learn - Artificial Intelligence Augmented Chemistry for Molecular Simulations and Beyond
$650,000Pratyush Tiwary · University Of Maryland, College Park · · FY2021 · MPS
ASCENT: Collaborative Research: Scaling Distributed AI Systems based on Universal Optical I/O
$650,000Vladimir M Stojanovic · University Of California-Berkeley · · FY2020 · ENG
NSF Convergence Accelerator Track L: Innovative chemical microsensor development for in situ, real-time monitoring of priority water pollutants to protect water quality
$649,984Cara M Santelli · University Of Minnesota-Twin Cities · · FY2024 · TIP
Developing the Blue-Collar AI Workforce
$649,983Samar Swaid · Broward College · · FY2024 · EDU
Early Detection of Hepatocellular Carcinoma
$649,953Laura Beretta · University Of Tx Md Anderson Can Ctr · R01 · FY2023 · CA
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** HOW CAN YOU MANAGE WHAT YOU DON'T MEASURE? THIS QUESTION FROM THE LATE PETER DRUCKER, FAMOUS BUSINESS MANAGEMENT CONSULTANT, IS CRITICAL IN THE DISCOVERY PROCESS OF FINDING EFFICIENCIES IN BUSINESS. PRECISION AGRICULTURAL SYSTEMS HAVE USHERED IN A NEW ERA BY INCORPORATING BIG DATA ANALYTICS TO INCREASE CROP YIELDS BY INTEGRATING SATELLITE IMAGERY, CLIMATE DATA, AND SENSOR TECHNOLOGIES FOR TARGETED APPLICATION OF FERTILIZERS, HERBICIDES, AND PESTICIDES. ALTHOUGH CROP PRODUCTION HAS BENEFITED FROM THIS TECHNOLOGICAL TRANSFORMATION, FORAGE-BASED LIVESTOCK PRODUCTION SYSTEMS LAG BEHIND.LIVESTOCK PRODUCTION SYSTEMS SUCH AS THE BEEF COW/CALF AND SHEEP INDUSTRIES HAVE NOT ADVANCED IN PRECISION AGRICULTURE LIKE OTHERSECTORS. EFFECTIVE GRASSLAND MONITORING PLANS OFTEN REQUIRE DATA COLLECTION BY HAND TO MEASURE FORAGE QUANTITY AND QUALITY. THIS IS OFTEN TIME AND LABOR PROHIBITIVE FOR LIVESTOCK PRODUCERS WHO OPERATE ON EXTENSIVE LANDSCAPES. THERE IS A CRITICAL NEED TO DEVELOP TOOLS THAT ENABLE PRODUCERS TO MONITOR FORAGE RESOURCES IN REAL-TIME AT THE INDIVIDUAL PASTURE SCALE WHERE MANAGEMENT DECISIONS ARE MADE. FAILURE TO DEVELOP A REAL-TIME MONITORING TOOL WILL RESULT IN OVER-GRAZING OF FORAGE RESOURCES, ECOSYSTEM DEGRADATION, DECREASED RANGELAND AND ANIMAL PRODUCTION, AND REDUCED RESILIENCY TO CLIMATE CHANGE.OUR LONG TERM GOAL IS TO TRANSFORM LIVESTOCK PRODUCTION BY DEVELOPING TOOLS TO ENABLE PRODUCERS TO LEVERAGE BIG DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE (AI) TO INFORM REAL-TIME MANAGEMENT DECISIONS WITHIN EXTENSIVE GRASSLAND SYSTEMS. THE OBJECTIVE OF THIS PROPOSAL IS TO INCORPORATE DATA PROCESSING PIPELINES WITH AI MODELS AND CITIZEN SCIENCE-COLLECTED DATA INTO A WEB APPLICATION ALLOWING PRODUCERS TO MONITOR FORAGE QUALITY AND QUANTITY IN REAL-TIME AT THE INDIVIDUAL PASTURE SCALE.THE RATIONALE FOR THIS PROJECT IS WHILE LARGE ADVANCEMENTS HAVE BEEN MADE INCORPORATING DATA ANALYTICS, AI, AND PRECISION AGRICULTURE, THERE HAS BEEN NO DOCUMENTED EFFORT TO DEVELOP REAL-TIME ESTIMATES OF FORAGE CONDITIONS FOR GRASSLAND LIVESTOCK PRODUCER USE. THE AREA OF THE U.S. DEDICATED TO FORAGE-BASED LIVESTOCK PRODUCTION IS SUBSTANTIAL, AND MONITORING OF THIS RESOURCE IS LIMITED. APPROXIMATELY 311 MILLION HA OF RANGELAND, 53 MILLION HA OF PASTURELAND, AND 16 MILLION HA OFHAYLAND (USDA-USFS, 2018; USDA-NRCS, 2003) SUPPORT LIVESTOCK COMPARED TO 160 MILLION HA OF ROW-CROP AGRICULTURE (USDA-NASS, 2019).THESE LANDSCAPES ARE THE MAIN SOURCE OF FORAGE FOR31.7 MILLION BEEF COWS, 3.8 MILLION BREEDING SHEEP, AND 2.2 MILLION BREEDING GOATS IN THE U.S. (USDA-NASS, 2018A, 2018B). THESE WORKING LANDSCAPES OFFER A VARIETY OF ECOSYSTEM GOODS AND SERVICES SUCH AS RECREATION, WILDLIFE HABITAT, BIODIVERSITY, HYDROLOGIC FUNCTION FOR GROUND WATER RECHARGE, CARBON SEQUESTRATION, AND OPEN SPACE FOR AESTHETIC VALUE.EFFECTIVE GRASSLAND MANAGEMENT REQUIRES VEGETATION MONITORING PLANS TO ENSURE GRAZING UTILIZATION TARGETS ARE MET. THIS IS ESPECIALLY IMPORTANT LOOKING TOWARDS THE FUTURE ASCLIMATE CHANGE SCENARIOS PREDICT GREATER VARIABILITY IN RAINFALL PATTERNS. INTER-ANNUAL DIFFERENCES IN FORAGE PRODUCTION AND QUALITY CAN LEAD TO FORAGE RESOURCES BECOMING DEGRADED OR ALTERNATELY UNDERUTILIZED.THE CHALLENGE FOR EXTENSIVE RANGELANDS IS THE LARGE AMOUNT OF SAMPLING NEEDED FOR ACCURATE ESTIMATES OF FORAGE PRODUCTION, UTILIZATION, AND PLANT COMMUNITY CHANGES. INFORMATIVE MONITORING IS TIME AND LABOR PROHIBITIVE FOR MOST PRODUCERS WORKING ON LARGE SCALE PASTURES (HUNDREDS TO THOUSANDS OF HA). UTILIZING DATA SCIENCE TOOLS TO MONITOR FORAGE RESOURCES IS KEY TO THE SUSTAINABILITY OF GRASSLAND BEEF PRODUCTION IN THE FUTURE. THE SIGNIFICANCE OF THE PROPOSED RESEARCH IS THAT DEVELOPING A WEB APPLICATION USING REAL-TIME DATA TO INFORM LIVESTOCK PRODUCER DECISION MAKING WILL GREATLY IMPROVE ENVIRONMENTAL QUALITY, BUILD CLIMATE RESILIENCY, AND INCREASE RANCH INCOME AND SUSTAINABILITY ACROSS MILLIONS OF HA OF AGRICULTURAL LANDS IN THE U.S.
$649,948South Dakota State University · · FY2022 · National Institute of Food and Agriculture
THANKS TO TECHNOLOGICAL ADVANCES, ANIMAL GENETICISTS HAVE AN EVER-EXPANDING TOOL CHEST WITH WHICH TO STUDY THE INHERITANCE OF TRAITS IN LIVESTOCK IN ORDER TO IMPROVE PRODUCTION. OUR LONG-RANGE GOAL IS TO DEVELOP INTEGRATED RESOURCES THAT LEVERAGE PRIOR INVESTMENTS IN CYBERINFRASTRUCTURE TO HELP MAXIMIZE THE UTILITY OF GENOTYPE-TO-PHENOTYPE DATA TO FUNCTIONALLY ANNOTATE LIVESTOCK GENOMES. THE OBJECTIVES OF THIS PARTICULAR APPLICATION ARE: 1) DEVELOPMENT OF MACHINE LEARNING-ASSISTED DATA CURATION AND AUTOMATED SEMANTIC ANNOTATION, AND 2) MANUAL CURATION OF GENOTYPE/PHENOTYPE, CORRELATION, AND HERITABILITY DATA. WITH THE GROWING VOLUME AND BREADTH OF INFORMATION, IT IS INCREASINGLY DIFFICULT FOR CURATORS TO KEEP ABREAST OF PUBLICATIONS. THESE COMPLEMENTARY OBJECTIVES TARGET THE NEED TO EFFICIENTLY COLLECT AND COMPREHEND LARGE AMOUNTS OF GENOTYPE/PHENOTYPE ASSOCIATION AND CORRELATION/HERITABILITY DATA THAT ARE BEING PUBLISHED AT AN ACCELERATING RATE. FIRST, WE EXPECT TO BEGIN TO AUTOMATE THE FUNCTIONAL ANNOTATION OF LIVESTOCK GENOMES BY APPLYING ARTIFICIAL INTELLIGENCE TECHNIQUES TO THE CURATION OF PUBLISHED QTL/VARIANT ASSOCIATION DATA INTO THE ANIMAL QTLDB, AND GENETIC AND PHENOTYPIC CORRELATION AND HERITABILITY DATA INTO THE ANIMAL CORRDB, FOR MULTIPLE LIVESTOCK SPECIES. SECOND, WE EXPECT TO DEVELOP ARTIFICIAL INTELLIGENCE TOOLS TO EXPEDITE ONTOLOGY DEVELOPMENT. THIRD, WE EXPECT TO DEVELOP INTELLIGENT RETRIEVAL TOOLS THAT CAN ANSWER QUERIES SEMANTICALLY. FOURTH, WE EXPECT TO CURATE GENOTYPE/PHENOTYPE AND CORRELATION/HERITABILITY DATA AND TO EXPAND RELEVANT ONTOLOGIES. TAKEN TOGETHER, OUR EFFORTS ARE EXPECTED TO GENERATE POSITIVE LONG-TERM EFFECTS ON RESEARCHERS' ABILITY TO TRANSFER KNOWLEDGE AND ANALYZE QTL/ASSOCIATION DATA TO ADDRESS ISSUES OF ECONOMIC AND HEALTH IMPORTANCE IN LIVESTOCK SPECIES.
$649,941Iowa State University Of Science And Technology · · FY2022 · National Institute of Food and Agriculture
NSF Convergence Accelerator Track L: Intelligent Nature-inspired Olfactory Sensors Engineered to Sniff (iNOSES)
$649,930Joanna Aizenberg · Harvard University · · FY2024 · TIP
Conservation of Total Synaptic Weights by Heterosynaptic Potentiation and Depression
$649,903Denis Pare · Rutgers University New Brunswick · · FY2002 · BIO
Label-free cell viability assays using Phase Imaging with Computational Specificity
$649,893Catalin Chiritescu · Phi Optics, Inc. · R44 · FY2023 · GM
From Human-Powered to Automated Video Description for Blind and Low Vision Users
$649,872Pooyan Fazli · Arizona State University-Tempe Campus · R01 · FY2023 · EY
Hypothesis Formation and Testing in an Interpretive Domain: a Model and Intelligent Tutoring System
$649,810Kevin D Ashley · University Of Pittsburgh · · FY2004 · CSE
**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** ARTIFICIAL INTELLIGENCE (AI) ADVANCES HAVE REVOLUTIONIZED INDUSTRIES (E.G., MANUFACTURING, MEDICINE); YET, THE POWER OF AI REMAINS ELUSIVE FOR MOST AGRICULTURE STAKEHOLDERS. THIS IS EXACERBATED BY TECHNOLOGISTS, WHICH TEND TO FOCUS ON TECHNOLOGY AND SOLUTIONS, AS OPPOSED TO STAKEHOLDERS AND PROBLEMS. THIS PROJECT WILL ADVANCE DECISION INTELLIGENCE (DI) AS A STAKEHOLDER- AND PROBLEM-FIRST APPROACH. DI, WHICH HAS BEEN USED EFFECTIVELY IN INDUSTRY FOR DECADES BUT NOT FORMALIZED ACADEMICALLY, WILL ENABLE NEW WAYS TO LINK AI TECHNOLOGY SOLUTIONS WITH STAKEHOLDER EXPERTISE AND DOMAIN-SPECIFIC MODELS, VISUALIZATION TOOLS, AND BUILT-IN METHODS OF IDENTIFYING BIASES. THROUGH PARTICIPATORY DESIGN WITH STAKEHOLDERS, AND USING THE SWEETPOTATO SUPPLY CHAIN AS A USE-CASE, THE PROJECT'S OBJECTIVES ARE TO: 1) CREATE SOFTWARE FOR VISUALIZING DATA-DRIVEN DECISIONS; 2) CONDUCT EXPERIMENTS TO IMPROVE USER DECISION MAKING; 3) IDENTIFY SOURCES OF BIAS THAT WOULD REDUCE TECHNOLOGY ACCESS AND ADOPTION; AND 4) DEVELOP AN OPEN-SOURCE SOFTWARE REFERENCE ARCHITECTURE FOR DI TOOLS. THIS PROJECT WILL PROVIDE A FORMALIZED METHODOLOGY FOR USERS TO IDENTIFY AND IMPLEMENT TECHNOLOGY SOLUTIONS IN EVOLUTIONARY AND TRACTABLE WAYS, AND ADDRESSES THE DSFAS-AI TOPICS OF FACILITATING REAL-TIME DECISION MAKING, INCORPORATING NEW METHODS TO REDUCE BIAS IN MACHINE LEARNING METHODS, AND DEVELOPING OPEN-SOURCE PLATFORMS FOR IMPROVED ADOPTION OF AI TOOLS. WITH INCREASING INNOVATIONS IN ON-FARM AI CAPABILITIES, A RISING TIDE OF DATA IS PRIMED TO OVERWHELM STAKEHOLDERS WITHOUT PROPER TOOLS. STEMMING THE TIDE WILL REQUIRE INNOVATION IN SENSING, AI, SOFTWARE, COGNITION, AND AGRICULTURE--ALL AREAS THE PROJECT TEAM COMPRISES EXPERTISE--INDICATING THE PROJECT IS TIMELY AND THE TEAM IS WELL-SUITED TO MEET PRODUCERS' NEEDS.
$649,722North Carolina State University · · FY2022 · National Institute of Food and Agriculture
iPAT:Intelligent Diet Quality Pattern Analysis for Harmonized MA-National Trials
$649,670Hua Fang · University Of Massachusetts Dartmouth · R01 · FY2023 · DK
Tailored Drug Titration through Artificial Intelligence
$649,649Gabriela Voskerician · Optima Integrated Health, Inc. · R44 · FY2018 · HL
RAPIDS2: A SCIDAC INSTITUTE FOR COMPUTER SCIENCE, DATA, AND ARTIFICIAL INTELLIGENCE
$649,623Northwestern University · · FY2020 · Department of Energy
Fast-Track to Success: Bridging the CTE and Higher Education to Empower Students for Immediate Career Opportunities in Technical Education (CTE & HE)
$649,606Reza Kamali-Sarvestani · California State University San Marcos Corporation · · FY2024 · EDU
NSF Convergence Accelerator Track L: An Integrated and Miniaturized Opioid Sensor System: Advancing Evidence-Based Strategies for Addressing the Opioid Crisis
$649,585Xinyu Zhang · Auburn University · · FY2024 · TIP
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** BETTER DATA ANALYSIS MEANS BETTER FOOD SAFETY. IN RECENT YEARS, ARTIFICIAL INTELLIGENCE (AI)-POWERED SOLUTIONS ARE EMERGING IN AGRICULTURE AND FOOD SCIENCE DUE TO THEIR SUPERIOR PATTERN RECOGNITION AND PREDICTION CAPABILITY. INTEGRATION OF FOOD SCIENCE AND AI CAN HELP ADDRESS NEW CHALLENGES SUCH ASINCREASING VOLUME OF DATA ACQUIRED FROM MIXED SAMPLES AND COMPLEX FOOD MATRICES, WHICH POSES CHALLENGES TO DATA ANALYSIS USING TRADITIONAL METHODS.THE OVERALL GOAL IS TOINTEGRATE NOVEL MACHINE LEARNING (ML) METHODS, SUCH ASATTENTION-BASED DEEP NETWORKS,WITHSURFACE-ENHANCED RAMAN SPECTROSCOPY (SERS) PLATFORM FORMULTIPLEXDETECTION AND QUANTIFICATIONOF FOOD CONTAMINANTS WITH HIGH ACCURACY.SPECIFIC OBJECTIVES ARE TOSYNTHESIZE NANOSUBSTRATES AND ACQUIRE SERS SPECTRAL DATA OF DIFFERENT TYPES AND QUANTITIES OF PESTICIDES BY SERS MEASUREMENT;DEVELOPATTENTION-BASEDDEEP LEARNING PREDICTION METHODS FOR QUALITATIVE AND QUANTITATIVE ANALYSIS OF SINGLE FOOD CONTAMINANTS AND MULTIPLE/MIXED FOOD CONTAMINANTS; VALIDATE AND ASSESS SERS-ML TECHNIQUES FOR MULTIPLEX DETECTION OF CHEMICAL CONTAMINANTS IN FRESH PRODUCE; AND ESTABLISH PROTOCOLS/DATABASES FOR MEASURING FOOD CONTAMINANTS.THIS STUDY WILL BE THE FIRST SYSTEMATICINVESTIGATION OF PESTICIDES IN FRESH PRODUCE BY SERS COUPLED WITH MACHINE LEARNING ALGORITHMS, WHICH WILL BE MORE ACCURATE, SENSITIVE, AND RELIABLE THAN CURRENT METHODS. THE PROJECT WILL BROADEN THE APPLICATIONS OF DATA SCIENCE IN MAINTAINING THE SUSTAINABILITY OF U.S. AGRICULTURE AND FOOD SYSTEMS.
$649,483University Of Missouri System · · FY2023 · National Institute of Food and Agriculture
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** THIS PROJECT AIMS TO ENCOURAGE INNOVATORS, RESEARCHERS, PRACTITIONERS, AND POLICY MAKERS TO RESPOND TO THE SOCIAL AND ETHICAL CHALLENGES OF BIG DATA AND ARTIFICIAL INTELLIGENCE (AI) IN PRECISION AGRICULTURE (PA) AND TO GENERATE INSIGHTS AND STRATEGIES THAT ARE SUITABLEFOR STAKEHOLDERS ACROSS THE PA VALUE CHAIN.THE PROJECT HAS THREE PRIMARY OBJECTIVES: (1) MAP STAKEHOLDERS' PERCEPTIONS AND EXPECTATIONS ABOUT THE SOCIETAL IMPLICATIONS OF BIG DATA AND AI IN PA; (2) DEPLOY DIGITAL SERIOUS GAMES TO UNDERSTAND FARMERS' AND FARM ADVISORS' RISK AND INFORMATION PREFERENCES IN RESPONSE TO DIFFERENT LEVELS OF AI RELIABILITY AND UNCERTAINTY, AND PREFERENCES TO FORMS OF FARM DATA OWNERSHIP, AND (3) CREATE OPPORTUNITIES FOR RESPONSIBLE INNOVATION IN PA THROUGH INTERDISCIPLINARY EDUCATION, POLICY RECOMMENDATIONS, AND OUTREACH ACTIVITIES.KEY PROJECT OUTCOMES INCLUDE: (1) SYNTHESIZED INFORMATION ABOUT THE CHALLENGES AND OPPORTUNITIES OFFERED BY BIG DATA AND AI IN PA; (2) IDENTIFIED STRATEGIES FOR RESPONSIBLE INNOVATION; (3) INTEGRATING SOCIETAL IMPLICATIONS OF PA TECHNOLOGIES INTO EXISTING UNIVERSITY COURSES, (4) DATA SCIENCE FOR PUBLIC GOOD SUMMER TRAINING PROGRAM, AND (5) AN INTERACTIVE SCIENCE MUSEUM EXHIBIT AT THE SCIENCE MUSEUM OF WESTERN VIRGINIA IN ROANOKE, VIRGINIA. PROJECT OUTCOMES WILL BE DISSEMINATED THROUGH PEER-REVIEWED MANUSCRIPTS, POLICY BRIEFS, AND CONFERENCE PRESENTATIONS.THE PROJECT USES A TRANSPARENT, INCLUSIVE, AND ITERATIVE APPROACH TO PROVIDE LEGITIMACY TO INNOVATION AND POLICY PROCESSES AND OUTCOMES, AND HAS THE POTENTIAL TO STIMULATE LEARNING AND REFLECTION AMONG STAKEHOLDERS IN THE U.S. FOOD AND AGRICULTURAL SYSTEMS.THE PROPOSED WORK ADVANCES THE ECONOMIC AND SOCIAL IMPLICATIONS OF FOOD AND AGRICULTURAL TECHNOLOGIES PROGRAM AREA (PRIORITY CODE A1642).
$649,396Virginia Polytechnic Institute & State University · · FY2023 · National Institute of Food and Agriculture
Understanding the impact of offloading with AI on exam performance and assessment validity
$649,388Chun-Kit J Chan · Iowa State University · · FY2025 · SBE
Creating an artificial intelligence therapy-to-data feedback loop for child developmental healthcare
$649,383Dennis Paul Wall · Stanford University · R01 · FY2021 · LM
Novel Integrative Approach for the Early Detection of Lung Cancer using Repeated Measures
$649,247Fabien Maldonado · Vanderbilt University Medical Center · R01 · FY2025 · CA
CICI: SSC: SciTrust: Enhancing Security for Modern Software Programming Cyberinfrastructure
$649,156Yanfang Ye · West Virginia University Research Corporation · · FY2018 · CSE
CAREER: Emotion Artificial Intelligence in the Future of Work: A Privacy and Relational Ethics Lens
$649,124Nazanin Andalibi · Regents Of The University Of Michigan - Ann Arbor · · FY2023 · CSE