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48,453 grants matching “machine learning”
Toward the molecular unpacking of the PaaC domain, a novel Plasmodesmata-association & activation Cassette
$800,000Jung-Youn Lee · University Of Delaware · · FY2018 · BIO
Collaborative Research: Frameworks: Convergence of Bayesian inverse methods and scientific machine learning in Earth system models through universal differentiable programming
$800,000Sri Hari Krishn Narayanan · University Of Chicago · · FY2021 · CSE
Collaborative Research: DMREF: Multi-material digital light processing of functional polymers
$800,000Adarsh Krishnamurthy · Iowa State University · · FY2023 · 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.** THIS RESEARCH WILL GENERATE NEW KNOWLEDGE REGARDING THE GLOBAL IMPLICATIONS OF THE RUSSIAN INVASION OF UKRAINE FOR AGRICULTURAL TRADE AND VALUE CHAINS, COMMODITY PRICES, AND CROSS-BORDER INVESTMENT. BEYOND THE SEVERE HUMANITARIAN CONSEQUENCES, THE WAR IN UKRAINE HAS FAR-REACHING ECONOMIC IMPLICATIONS FOR GLOBAL AGRICULTURAL AND FOOD MARKETS. BOTH COUNTRIES EXPORT STAPLE GRAINS, OILSEEDS, VEGETABLE OIL AND MEAL, FERTILIZER, AND ENERGY PRODUCTS. TO PROVIDE ROBUST QUANTITATIVE EVIDENCE ON THE SHORT-RUN AND LONG-RUN TRADE AND VALUE CHAIN IMPLICATIONS AND THE REGION-SPECIFIC GLOBAL REALLOCATION AND WELFARE EFFECTS, WE WILL USE A NEWLY CONSTRUCTED DATASET ON TRADE SANCTIONS AND EXPORT BANS IMPOSED IN RESPONSE TO THE RUSSIA-UKRAINE WAR, HIGH-FREQUENCY TRADE, AND DETAILED VALUE CHAINDATA, AND THEORY-CONSISTENT EMPIRICAL MODELS. INSIGHTS FROM THIS APPLIED TRADE ANALYSIS WILL INFORM AN EMPIRICAL ASSESSMENT OF COMMODITY PRICES BUILDING ON ADVANCED MACHINE LEARNING TECHNIQUES AND FOOD SECURITY IMPLICATIONS USING NATIONAL AND HOUSEHOLD-LEVEL FOOD SECURITY DATA FROM DEVELOPING COUNTRIES. WE WILL UTILIZE DETAILED FIRM-LEVEL INVESTMENT DATA AND STATE-OF-THE-ART CAUSAL INFERENCE METHODS TO SHED LIGHT ON THE GLOBAL IMPLICATIONS OF ECONOMIC SANCTIONS AND INVESTMENT UNCERTAINTY FOR CROSS-BORDER CAPITAL FLOWS IN THE AGRICULTURAL AND FOOD INDUSTRY. THIS ANALYSIS OF THE ECONOMIC CONSEQUENCES OF RUSSIA'S INVASION OF UKRAINE WILL PROVIDE ROBUST QUANTITATIVE EVIDENCE REGARDING A CRITICAL DRIVER OF GLOBAL TRADE, COMMODITY PRICE, AND INVESTMENT UNCERTAINTY. CONSEQUENTLY, THE PROJECT WILL DELIVER ESSENTIAL KNOWLEDGE ON THE FUNCTIONING OF AGRICULTURAL MARKETS UNDER ECONOMIC AND TRADE UNCERTAINTY AND ENHANCE MARKET EFFICIENCY AND PERFORMANCE. INSIGHTS DRAWN FROM THIS RESEARCH WILL HELP INFORM FEDERAL POLICIES THAT FOSTER THE COMPETITIVENESS OF U.S. FARMERS AND RANCHERS AND INCREASE THEIR PARTICIPATION AND SUCCESS IN GLOBAL AGRICULTURAL AND FOOD MARKETS.
$800,000North Dakota State University · · FY2023 · National Institute of Food and Agriculture
Collaborative Research: RI:Medium:MoDL:Mathematical and Conceptual Understanding of Large Language Models
$800,000Sanjeev Arora · Princeton University · · FY2022 · CSE
SLES: Vision-Based Maximally-Symbolic Safety Supervisor with Graceful Degradation and Procedural Validation
$800,000Jia Deng · Princeton University · · FY2023 · CSE
NeuroNex Innovation Award: A National Resource for Mesoscale and Connectomic Brain Mapping
$799,999Narayanan Kasthuri · University Of Chicago · · FY2017 · BIO
** 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 DEVELOP A SELECTION METHOD THAT SYNERGISTICALLY COMBINES HIGH-THROUGHPUT PHENOTYPING (HTP) AND GENOMIC SELECTION (GS) TO ACCELERATE YIELD IMPROVEMENT IN PUBLIC SMALL GRAINS BREEDING PROGRAMS WITH MODEST BUDGETS.OUR PROPOSED METHOD, 'PHENOMIC ASSISTED GENOMIC SELECTION' (PAGS), USES HTP TO IMPUTE YIELD PHENOTYPIC DATA ON A PROPORTION OF RESEARCH PLOTS. BOTH IMPUTED AND TRUE YIELD DATA ARE SUBSEQUENTLY USED FOR GS MODEL TRAINING. PAGS ENABLES BREEDERS TO GENERATE LARGE GS MODEL TRAINING DATASET SETS REQUIRED FOR SUCCESSFUL GS AMONG UNTESTED BREEDING CANDIDATES, UNLOCKING THE POTENTIAL OF GS TO SHORTEN BREEDING CYCLES AND ACCELERATE RATES OF GENETIC GAIN.TO DEVELOP PAGS, WE WILL GENERATE AND ANALYZE YIELD AND HTP DATA TO TEST THE LIMITS OF GRAIN YIELD IMPUTATION USING HTP DATA AND IDENTIFY THE BEST STATISTICAL OR MACHINE LEARNING MODEL FOR THIS PURPOSE. NEXT, WE WILL CONDUCT VALIDATION STUDIES TO EVALUATE THE EFFECT OF INCLUDING IMPUTED YIELD DATA ON GS ACCURACY UNDER DIFFERENT SCENARIOS. LASTLY, WE WILL USE STOCHASTIC SIMULATIONS TO EVALUATE THE COSTS AND BENEFITS OF PAGS TO ULTIMATELY EVALUATE ITS MERIT COMPARED TO ALTERNATIVE STRATEGIES.
$799,999University Of Illinois · · FY2023 · National Institute of Food and Agriculture
Cognitive Computing of Alzheimer's Disease Genes and Risk
$799,998Olivier Lichtarge · Baylor College Of Medicine · U01 · FY2023 · AG
Cognitive Computing of Alzheimer's Disease Genes and Risk
$799,998Olivier Lichtarge · Baylor College Of Medicine · U01 · FY2024 · AG
Cognitive Computing of Alzheimer's Disease Genes and Risk
$799,998Olivier Lichtarge · Baylor College Of Medicine · U01 · FY2021 · AG
The Brain Basis of Emotion: A Category Construction Problem
$799,998Ajay Satpute · Northeastern University · · FY2020 · SBE
Cognitive Computing of Alzheimer's Disease Genes and Risk
$799,998Olivier Lichtarge · Baylor College Of Medicine · U01 · FY2022 · AG
tRNA-derived RNA Fragments (tRF) as Prognostic and Diagnostic Biomarkers for Alzheimerâs Disease
$799,998Xiaoyong Bao · University Of Texas Med Br Galveston · R61 · FY2024 · AG
tRNA-derived RNA Fragments (tRF) as Prognostic and Diagnostic Biomarkers for Alzheimerâs Disease
$799,998Xiaoyong Bao · University Of Texas Med Br Galveston · R61 · FY2023 · AG
Expeditions: Collaborative Research: Understanding the World Through Code
$799,995Yisong Yue · California Institute Of Technology · · FY2020 · CSE
SitS NSF-UKRI: Rapid Deployment of Multi-Functional Modular Sensing Systems in the Soil
$799,995David Frost · Georgia Tech Research Corporation · · FY2019 · ENG
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** PORCINE REPRODUCTIVE AND RESPIRATORY SYNDROME VIRUS-TYPE 2 (PRRSV-2) IS A RAPIDLY EVOLVING RNA VIRUS IMPACTING ~30-50% OF BREEDING FARMS. SWINE PRODUCERS SEQUENCE THOUSANDS OF VIRUSES ANNUALLY AS PART OF DISEASE MANAGEMENT, BUT ARE FRUSTRATED BY LIMITATIONS IN TRANSLATING GENETIC DATA TO PHENOTYPIC INSIGHTS RELEVANT FOR DECISION-MAKING. OUR OBJECTIVE IS TO CREATE AN INTEGRATIVE DATA SCIENCE PLATFORM TO PREDICT THE IMMUNOGENIC AND EPIDEMIOLOGIC PHENOTYPE OF PRRSV-2 VARIANTS THROUGH BREAKING BARRIERS AND BUILDING CONNECTIVITY AMONG AI/ML ACROSS STRUCTURAL BIOLOGY, COMPUTATIONAL IMMUNOLOGY, AND GENOMIC EPIDEMIOLOGY.SPECIFICALLY, WE WILL APPLY UNSUPERVISED LEARNING TO IDENTIFY CLUSTERS (VARIANTS) OF CLOSELY RELATED GENETIC SEQUENCES WITHIN LARGE-SCALE DATABASES. WE WILL MEASURE STRUCTURAL DIVERGENCE BETWEEN VARIANTS (HYPOTHESIZED TO INFLUENCE IMMUNOGENICITY AND ANTIBODY BINDING) USING ALPHAFOLD 2.0, AN ARTIFICIAL INTELLIGENCE TOOL RECENTLY DEVELOPED FOR 3D STRUCTURAL PREDICTIONS OF PROTEIN FOLDING. WE WILL THEN APPLY MACHINE LEARNING TO EXPERIMENTAL DATA ON ANTIBODY CROSS-REACTIVITY AMONGST VARIANTS TO BUILD A MODEL THAT PREDICTS ANTIGENIC DISTANCE BASED ON GENETIC DISSIMILARITIES AND STRUCTURAL DIVERGENCE METRICS. LASTLY, WE WILL DEVELOP A MACHINE LEARNING ALGORITHM THAT PREDICTS A VARIANT'S EPIDEMIOLOGIC FITNESS (I.E., EXPANSION OF A GIVEN VARIANT THROUGH TIME) BASED ON ANTIGENIC DISTANCE, STRUCTURAL DIVERGENCE, AND PHYLOGENETIC FEATURES.WE WILL ALSO CREATE AN HTML-BASED VISUALIZATION PLATFORM THAT ALLOWS STAKEHOLDERS TO CLASSIFY AND CONTEXTUALIZE THEIR OWN SEQUENCES WITHIN THE BROADER PHENOTYPIC DIVERSITY OF THE VIRUS. THIS INTEGRATION OF BACK-END ANALYTICAL METHODS AND FRONT-END VISUALIZATION TOOLS WILL IMPROVE HUMAN-DATA INTERACTIONS SURROUNDING INTERPRETATION OF SEQUENCE DATA AND SUPPORT DECISION-MAKING ABOUT APPROPRIATE DISEASE MANAGEMENT AND IMMUNIZATION PRACTICES WITHIN THE SWINE INDUSTRY.
$799,993Regents Of The University Of Minnesota · · FY2023 · National Institute of Food and Agriculture
Collaborative Research: CNS Core: Medium: Analytics and Online Optimization at Scale for Cellular Networks
$799,991Sanjay Shakkottai · University Of Texas At Austin · · FY2021 · CSE
Expand QISE: Track 1: RLQSC: Reinforcement Learning for the Optimal Design of Programmable Quantum Sensor Circuit
$799,985Sathish Kumar · Cleveland State University · · FY2022 · 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.** NATIONAL AGRICULTURAL STATISTICS SERVICE (NASS) CONDUCTS WEEKLY SURVEYS OF CROP AND SOIL MOISTURECONDITIONS FOR U.S. CROPLAND AND PROVIDES COARSE-RESOLUTION SATELLITE SOIL MOISTURE ANDVEGETATION CONDITIONS VIA A WEB APPLICATION CROP-CASMA. HOWEVER, ITS COARSE-RESOLUTIONMAPS ARE UNABLE TO CAPTURE FIELD/SUBFIELD LEVEL SOIL MOISTURE VARIATIONS. IT IS URGENTLY NEEDED TODEVELOP FIELD/SUBFIELD-LEVEL SOIL MOISTURE MAPS FOR NASS AND THE AGRICULTURAL COMMUNITY TO MONITORCROP GROWTH CONDITIONS AND ASSESS DROUGHT OR FLOOD IMPACT. THIS PROJECT WILL ESTABLISH A PARTNERSHIPBETWEEN US AND CANADIAN INSTITUTES TO DEVELOP NEW MACHINE LEARNING HIGH-RESOLUTION SOILMOISTURE (ML-HRSM2.0) PRODUCTS IN SUPPORT OF NASS CROP MONITORING AND ASSESSMENT. IN SITUNETWORKS, SATELLITE IMAGERY AND MODEL-DERIVED WEATHER, SOIL MOISTURE, TERRAIN, AND SOIL MAPS WILL BECOMBINED TO PREDICT DAILY SOIL WATER CONTENT (SWC) AND PLANT AVAILABLE WATER STORAGE (PAWS) AT 100-M AT THE SURFACE AND ROOTZONE SINCE 2016. WE WILL COMBINE ML MODELS WITH A PROCESS MODEL VIA DATAASSIMILATION TO DEVELOP CROP SOIL MOISTURE CONDITION MAPSAND NASS WEEKLY SOIL MOISTURE CONDITION REPORTS AND DISSEMINATE ALLMAPS OVER CROP-CASMA FOR ENHANCING NASS SOIL MOISTURE CONDITION MONITORING OPERATION ANDFOR FREE PUBLIC USE. IT IS EXPECTED THAT USING ML-HRSM2.0 PRODUCT WILL HELP NASS AND THE AGRICULTURAL COMMUNITY IMPROVE CROP CONDITION MONITORING, DISASTER ASSESSMENT, ANDOPERATIONAL DECISION MAKINGS.
$799,972University Of Wisconsin System · · FY2023 · National Institute of Food and Agriculture
Integrating the Local and Global Structure of Natural Scenes
$799,951Michael S Lewicki · Carnegie Mellon University · · FY2007 · CSE
Collaborative Research: RI: Medium: MoDL: Occams Razor in Deep and Physical Learning
$799,942Pratik A Chaudhari · University Of Pennsylvania · · FY2022 · CSE
Collaborative Research: III: Medium: Contextualized and Multimodal Foundation Models for Graph Data in Scientific Discovery
$799,941Rex Ying · Yale University · · FY2024 · CSE
Collaborative Research: CPS: Medium: Spatio-Temporal Logics for Analyzing and Querying Perception Systems
$799,936Yezhou Yang · Arizona State University · · FY2021 · CSE