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48,453 grants matching “machine learning”
**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 RESEARCH WILL GENERATE NEW KNOWLEDGE REGARDING THE FORMATION OF PREFERENTIAL TRADE AGREEMENTS (PTAS), THEIR IMPACT ON GLOBAL TRADE, AND THE CONSEQUENCES FOR U.S. AGRICULTURAL AND FOOD BUSINESSES AND EMPLOYMENT. TO ACCOMPLISH THIS GOAL, WE WILL RELY ON MODERN STATISTICAL MODELING TECHNIQUES TO THOROUGHLY INVESTIGATE THE FACTORS THAT INFLUENCE THE FORMATION OF PTAS. THIS ANALYSIS BUILDS ON NEWLY COLLECTED PTA DATA CAPTURED WITH THE HELP OF NEURAL MACHINE TRANSLATION AND NATURAL LANGUAGE PROCESSING SYSTEMS. TO DETERMINE FACTORS THAT INFLUENCE THE FORMATION OF PTAS, WE WILL ADOPT THE RANDOM FOREST ALGORITHM. THIS STATISTICAL ANALYSIS WILL PROVIDE NEW INSIGHTS REGARDING THE ROLE OF ECONOMIC, SOCIAL, AND POLITICAL FACTORS IN FORMING PTAS WITH AGRICULTURAL AND FOOD PROVISIONS. WE WILL USE THE NEWLY CREATED DATASET TO INVESTIGATE THE IMPACT OF PTA PROVISIONS ON AGRICULTURAL AND FOOD TRADE IN THE SECTORAL THREE-WAY GRAVITY MODEL CONTEXT RELYING ON AN ADAPTATION OF THE PRIOR LEAST ABSOLUTESHRINKAGE AND SELECTION OPERATOR TO THE POISSON PSEUDO-MAXIMUM LIKELIHOOD ESTIMATOR. THIS INNOVATIVE MACHINE LEARNING APPROACH WILL ENABLE US TO INCORPORATE PRIOR INFORMATION, REDUCE OVER-FITTING, AND FACILITATE FEATURE SELECTION IN A HIGH-DIMENSIONAL CONTEXT. WE WILL ALSO ASSESS THE IMPACT OF PTA PROVISIONS ON THE STRUCTURE AND CONDUCT OF THE U.S. AGRICULTURAL AND FOOD SECTOR AND EVALUATE EMPLOYMENT EFFECTS. A BETTER UNDERSTANDING OF THESE TRADE POLICY CONSEQUENCES WILL SHED LIGHT ON A CRITICAL DRIVER OF STRUCTURAL CHANGE. SUCH KNOWLEDGE IS ESSENTIAL FOR THE FUNCTIONING OF GLOBAL SUPPLY CHAINS. THE PROJECT WILL HELP TO INFORM FEDERAL POLICIES THAT AIM TO FOSTER THE COMPETITIVENESS OF U.S. FARMERS AND RANCHERS AND INCREASE THEIR PARTICIPATION AND SUCCESS IN INTERNATIONAL MARKETS.
$649,888University Of Connecticut · · FY2022 · National Institute of Food and Agriculture
Sub-second neurochemistry of error signals and affective processing in depression
$649,886Pearl H Chiu · Virginia Polytechnic Inst And St Univ · R01 · FY2025 · MH
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** COFFEE IS ONE OF THE MOST IMPORTANT GLOBALLY-TRADED CROP COMMODITIES WITH A RETAIL VALUE OF $200 BILLION GLOBALLY. HAWAII IS THE MAIN COFFEE PRODUCING STATE IN THE US, AND IT IS OF HIGH AGRICULTURAL IMPORTANCE WITHIN THE STATE. IN 2022 A 17% INCREASE IN COFFEE PRODUCTION IS FORECASTED WITH AN ESTIMATED VALUE OF $60 MILLION AND A DOWNSTREAM ECONOMIC IMPACT VALUED AT $211 MILLION. BREEDING IN PERENNIAL FRUIT CROPS SUCH AS COFFEE INVOLVES HURDLES THAT ADD TO THE DIFFICULTIES OF MOST ANNUAL CROPS, INCLUDING LONG BREEDING CYCLES, LARGER AREAS REQUIRED FOR FIELD EVALUATION, SMALLER POPULATIONS, AND CHALLENGES IN MULTIPLYING INDIVIDUAL GENOTYPES TO REPLICATE IN SINGLE OR MULTIPLE ENVIRONMENTS. COFFEE HAS THE ADDED COMPLICATION OF BEING GROWN IN DIFFERENT AGRONOMIC SYSTEMS AND BIOPHYSICAL ENVIRONMENTS. THERE HAVE BEEN RELATIVELY FEW EFFORTS TO INTEGRATE HIGH-THROUGHPUT PHENOMICS INTO COFFEE SELECTION AND BREEDING, AND THIS PROJECT SEEKS TO FILL THAT GAP. THIS PROJECT WILL LEVERAGE A NEW COFFEE BREEDING PROGRAM BEING DEVELOPED BY WORLD COFFEE RESEARCH IN COLLABORATION WITH OTHER NATIONAL PROGRAMS AND RESEARCH ORGANIZATIONS INCLUDING THE USDA/ARS IN HILO HAWAII. WE WILL DEVELOP PHENOMIC METHODS TO BE APPLIED IN THIS NETWORK (AND ANY OTHER COFFEE BREEDING PROGRAMS), TO IMPROVE SELECTION AND INCREASE GENETIC GAIN IN COFFEE. SPECIFICALLY, THIS PROJECT WILL DEVELOP A SUITE OF MACHINE LEARNING ALGORITHMS TO RELATE RAPID HYPERSPECTRAL REFLECTANCE MEASUREMENTS TO KEY GAS EXCHANGE AND FOLIAR NUTRIENT COMPOSITION TRAITS AND YIELD, AS WELL AS RESILIENCE TO COFFEE RUST, DEVELOPED FROM DATA SPANNING MULTIPLE SITES, GROWTH ENVIRONMENTS AND SEASONS.
$649,882Ohio State University, The · · FY2023 · National Institute of Food and Agriculture
From Human-Powered to Automated Video Description for Blind and Low Vision Users
$649,872Pooyan Fazli · Arizona State University-Tempe Campus · R01 · FY2023 · EY
Multi-level modeling to inform interventions for control of multidrug-resistant organisms within healthcare networks
$649,871Eili Ya'akov Klein · Center/ Disease Dynamics, Econom/Policy · U01 · FY2018 · CK
Genetic Factors in Keratoconus
$649,860Yaron S Rabinowitz · Cedars-Sinai Medical Center · R01 · FY2008 · EY
Metabolic predictors of disease outcomes in multiple sclerosis
$649,829Kathryn C. Fitzgerald · Johns Hopkins University · R01 · FY2024 · NS
NSF Convergence Accelerator Track M: Distributed Flexible Strain Sensors to Enable Proprioceptive Cochlear Implant Electrodes
$649,810Maysamreza Chamanzar · Carnegie Mellon University · · FY2024 · TIP
Rapid fungal identification and antifungal susceptibility testing through quantitative, multiplexed RNA detection
$649,802Roby Paul Bhattacharyya · Broad Institute, Inc. · R01 · FY2023 · AI
Mechanistic Machine Learning
$649,799David J. Albers · University Of Colorado Denver · R01 · FY2018 · LM
Circadian rhythms and homeostatic sleep regulation during adolescence: Implications for reward, cognitive control, and substance use risk
$649,781Peter L Franzen · University Of Pittsburgh At Pittsburgh · P50 · FY2025 · DA
Collaborative Research: DMREF: Predicting Molecular Interactions to Stabilize Viral Therapies
$649,767Sarah L Perry · University Of Massachusetts Amherst · · FY2021 · MPS
**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
Simultaneous coaxial widefield imaging and reflectance confocal microscopy for improved diagnosis of skin cancers in vivo
$649,717David Lee Dickensheets · Montana State University - Bozeman · R01 · FY2020 · EB
Extraction of molecular signature of HFpEF via a machine learning-empowered proteomic characterization: A study of the BCAA pathway
$649,707Ding Wang · University Of California Los Angeles · R01 · FY2022 · HL
Extraction of molecular signature of HFpEF via a machine learning-empowered proteomic characterization: A study of the BCAA pathway
$649,707Ding Wang · University Of California Los Angeles · R01 · FY2020 · HL
Extraction of molecular signature of HFpEF via a machine learning-empowered proteomic characterization: A study of the BCAA pathway
$649,707Ding Wang · University Of California Los Angeles · R01 · FY2021 · HL
Extraction of molecular signature of HFpEF via a machine learning-empowered proteomic characterization: A study of the BCAA pathway
$649,707Ding Wang · University Of California Los Angeles · R01 · FY2019 · HL
High speed and wearable speckle contrast optical spectroscopy for cuffless blood pressure measurements
$649,678Darren Michael Roblyer · Boston University (Charles River Campus) · R01 · FY2025 · HL
Synthetic gene sensors and effectors to redirect organoid development
$649,606Roger D. Kamm · Massachusetts Institute Of Technology · R01 · FY2022 · EB
Biomechanical and Biological Predictors of Cartilage Health Following Meniscus Injury
$649,601Amy L McNulty · Duke University · R01 · FY2024 · AR
A deep learning framework for high-definition prediction and interpretation of protein localization
$649,588Dong Xu · University Of Missouri-Columbia · · FY2022 · BIO
Biomarkers and Genes Associated with Placental Development and Function in Response to Environmental Pollution
$649,585Sherin U. Devaskar · University Of California Los Angeles · R01 · FY2016 · HD
ENIGMA World Aging Center
$649,576Paul M Thompson · University Of Southern California · R01 · FY2022 · AG
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** OUR TEAM RECENTLY CREATED ATOOL THAT PROVIDES ROBUST SOIL HEALTH ASSESSMENTS TO HELP LAND STEWARDS WORK TOWARDS THEIR SUSTAINABILITY GOALS AND SUPPORT HEALTHY COMMUNITIES. HOWEVER, OUR CURRENT SUITE OF SOIL HEALTH INDICATORS ARE COSTLY AND TIME-CONSUMING TO MEASURE. WHILE WE ALSO HAVENOVEL DATARESOURCES SUCH AS SOIL SPECTRA AND MICROBIOMESAT OUR FINGERTIPS, THESE DATA HAVE YET TO BE INTEGRATED INTO OUR SOIL HEALTH DATABASE, PREDICTIONS, OR EDUCATION. THEREFORE, WEPROPOSE TO IDENTIFY NOVEL DATA RESOURCES THAT ALLOW FOR MORE AFFORDABLE, RAPID, AND COMPREHENSIVE SOIL HEALTH TESTING IN DIRECT RESPONSE TO THE DATA SCIENCE FOR FOOD AND AGRICULTURE SYSTEMS CALL TO SYNTHESIZE OR ANALYZE EXISTING DATA AND RESOURCES ON SOIL HEALTH. WE WILL FIRST INTEGRATE THESE NOVEL DATA STREAMS INTO OUR CURRENT SOIL HEALTH DATABASE, AND THENTEST WHETHER THESE NEW DATA RESOURCES CAN HELP USACCURATELY PREDICT SOIL HEALTH USING MACHINE AND DEEP LEARNING MODELS.FINALLY, WE WILLTRAIN UNDERGRADUATE AND GRADUATE STUDENTS THROUGH FORMALIZED DATA SCIENCE INTERNSHIPS, COURSEWORK, AND ASSISTANTSHIPS. THROUGH THISPROJECT, WE HELP TO DEVELOP A DEEPER UNDERSTANDING OF THE RELATIONSHIP BETWEEN THE SOILFINGERPRINT (I.E., MICROBIOME AND SPECTROSCOPY) AND ITS HEALTH STATUS, AN IMPROVED TOOL TO EFFECTIVELY MEASURE SOIL HEALTH, AND THE DEVELOPMENT OF A SKILLED WORKFORCE. OURCOMBINED EXPERTISE CREATES A UNIQUE OPPORTUNITY TO LEVERAGE EXISTING RESOURCES AND MAKE ADVANCEMENTS IN RESEARCH AND EDUCATION THAT WILL BENEFIT STUDENTS, LAND STEWARDS, THE NEXT GENERATION OF RESEARCHERS, AND THE COMMUNITY AT LARGE.
$649,570University Of Hawaii · · FY2023 · National Institute of Food and Agriculture