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15,895 grants matching “artificial intelligence”
Collaborative Research: NSF-BSF: CIF: Small: Error-Correcting Codes for Next-Generation Artificial Intelligence
$300,000Anxiao Jiang · Texas A&M Engineering Experiment Station · · FY2024 · CSE
EAGER: An AI-driven Paradigm for Collective and Collaborative Community Resilience in the COVID-19 Era and Beyond
$300,000Yanfang Ye · Case Western Reserve University · · FY2020 · CSE
AccelNet Design: Research and Education Accelerated by Connections in Clean Hydrogen (REACH2)
$300,000Jasna Jankovic · University Of Connecticut · · FY2024 · O/D
EAGER: STEM Illinois: The Land-Grant Model of Outreach and Education to Nurture Future Underrepresented Computer Scientists
$300,000Ruby Mendenhall · University Of Illinois At Urbana-Champaign · · FY2020 · CSE
Collaborative Research: CIF: Medium: MoDL:Toward a Mathematical Foundation of Deep Reinforcement Learning
$300,000Jason Lee · Princeton University · · FY2022 · CSE
Collaborative Research: Multi-Modal Sensing with Robust, Unified, and Scalable Diffractive Optical Neural Networks
$300,000Caiwen Ding · University Of Minnesota-Twin Cities · · FY2025 · ENG
TOPIC 389 - INTELLIGENT SOFTWARE FOR RADIATION THERAPY PLANNING
$300,000Jonathan Edelen · Radiasoft, Llc · N43 · FY2019 · CA
Collaborative Research: CISE-MSI: DP: IIS RI: Research Capacity Expansion via Development of AI Based Algorithms for Optimal Management of Electric Vehicle Transactions with Grid
$300,000Ganesh K Venayagamoorthy · Clemson University · · FY2023 · CSE
Collaborative Research: MATH-DT Dynamical Models and Statistical Guarantees for Sparse Sensing in Nuclear Digital Twins
$300,000Andrei A Klishin · University Of Hawaii · · FY2025 · MPS
Collaborative Research: SHF: Small: Reimagining Communication Bottlenecks in GNN Acceleration through Collaborative Locality Enhancement and Compression Co-Design
$300,000Tong Geng · University Of Rochester · · FY2023 · CSE
EAGER: SaTC-EDU: Privacy Enhancing Techniques and Innovations for AI-Cybersecurity Cross Training
$300,000Ling Liu · Georgia Tech Research Corporation · · FY2020 · EDU
EAGER: NAIRR Pilot Expansion: FA1: RUI: Bridging the Gap: Coalescing Research into Educational Labs for the Application of Machine Learning Methodologies to Cybersecurity Threats
$300,000Suleyman Uludag · Regents Of The University Of Michigan - Flint · · FY2025 · CSE
Development of a Machine Learning Model for Prostate Cancer Treatment Planning
$300,000Roberto Nicolalde · Nicolalde R And D, Llc · N43 · FY2019 · CA
Postdoctoral Fellowship: MPS-Ascend: Moire Engineering in van der Waals Heterostructures
$300,000Tara Pena · Pena, Tara · · FY2023 · MPS
Collaborative Research: Resolving the Contributions of Lattice Oxygen, Dioxygen, Acid Sites, Base Sites, and Redox Sites during the Oxidative Scission of Ketones on Metal Oxides
$300,000Jesse Q Bond · Syracuse University · · FY2024 · ENG
EAGER: SaTC-EDU: Identifying Educational Conceptions and Challenges in Cybersecurity and Artificial Intelligence
$300,000Atul Prakash · Regents Of The University Of Michigan - Ann Arbor · · FY2020 · EDU
Collaborative Research: Resolving the Contributions of Lattice Oxygen, Dioxygen, Acid Sites, Base Sites, and Redox Sites during the Oxidative Scission of Ketones on Metal Oxides
$300,000Andreas Heyden · University Of South Carolina At Columbia · · FY2024 · ENG
Using Conversational Agents to Foster Preschool Children's Science Learning and Engagement from Interactive Science Videos
$300,000Mark Warschauer · University Of California-Irvine · · FY2019 · EDU
ReDDDOT Phase 1: Planning Grant: Assessing Environmental Impacts of AI Through Participatory Methods
$300,000Tamara Kneese · Data & Society Research Institute · · FY2024 · TIP
AI-DCL: Governing bias in AI system with humans in the decision loop
$300,000Kai-Wei Chang · University Of California-Los Angeles · · FY2019 · CSE
Collaborative Research: Elements: VLCC-States: Versioned Lineage-Driven Checkpointing of Composable States
$300,000M Mustafa Rafique · Rochester Institute Of Tech · · FY2024 · CSE
EAGER: Clustering, Auto-Encoding, and Generative Modelling of 3D Object Representations for Manufacturing
$300,000George Kesidis · Pennsylvania State Univ University Park · · FY2024 · ENG
A GENERATION AGO, THE NEED FOR AGRONOMY KNOWLEDGE FOR CROP PRODUCTION AND ENVIRONMENTAL MANAGEMENT WAS ON A DOWNWARD TREND. COMPLICATED MIXES OF HERBICIDES AND CROP MANAGEMENT RECOMMENDATIONS SPECIFIC TO CERTAIN WEEDS AND SITUATIONS WERE LARGELY REPLACED BY A SIMPLE APPLICATION OF BROAD-SPECTRUM HERBICIDES TO GENETICALLY MODIFIED ORGANISM (GMO) CROPS THAT EFFECTIVELY REMOVED NEARLY ALL WEEDS. INSECT RESISTANCE WAS BRED INTO CROPS AND DRAMATICALLY REDUCED THE IMPACT OF SOME OF THE COSTLIEST PESTS; MAJOR CROP YIELDS WERE ON SUSTAINED UPWARD TRENDS. CROP PRODUCTION'S ENVIRONMENTAL IMPACT WAS REDUCED DUE TO SAFER PESTICIDES AND REDUCTIONS IN TILLAGE, WHICH LESSENED SOIL EROSION.UNFORTUNATELY, A NUMBER OF FACTORS TURNED IN THE LAST DECADE TO CHANGE HOW CROPS NEED TO BE PRODUCED. 1) WEEDS AND INSECTS HAVE DEVELOPED RESISTANCE TO BIOTECH TOOLS, AND A ONE-PRONGED APPROACH DOES NOT USUALLY WORK. 2) WHILE EROSION AND PESTICIDE EFFECTS IN THE ENVIRONMENT ARE LESS, A SURGE IN WATER QUALITY PROBLEMS POINTS BACK TO AGRICULTURE AND BLANKET-APPLIED FERTILIZERS. 3) CONSUMERS ARE INCREASINGLY DISSATISFIED WITH FOOD PRODUCTION AND OFTEN REJECT GMOS, CHEMICAL USE, AND MASS PRODUCTION TECHNIQUES; THEREFORE, THEY ARE CHANGING THEIR BUYING PREFERENCES. 4) FOOD INSECURITY IS INCREASING AROUND THE WORLD. 5) MANY FARMING OPERATIONS ARE ECONOMICALLY UNSTABLE, AND RURAL ECONOMIES ARE CONCURRENTLY SUFFERING.THE ONCOMING SUITE OF INFORMATION TECHNOLOGIES SHOWS PROMISE IN HELPING AGRICULTURE WITH ITS CHALLENGES. NEW SENSING TOOLS PROVIDE MORE DATA ABOUT CROP CHARACTERISTICS, AND WE CAN MOVE AROUND, STORE, AND PROCESS THIS INFORMATION WITH GREATER EASE. A KEY PART OF AN INFORMATION-INTENSE APPROACH TO CROP PRODUCTION IS THE USE OF VARIABLE RATE TECHNOLOGIES, WHERE CHARACTERISTICS ACROSS A FIELD INFORM A TAILORED APPROACH TO THE USE OF SEEDS, FERTILIZERS, SOIL AMENDMENTS, AND PESTICIDES. THIS APPROACH CAN HELP GROWERS ADDRESS THE COMPLEXITIES OF PRODUCTION ENVIRONMENTS AND THE UNIQUE NEEDS OF FOOD, FEED, FUEL, AND FIBER CONSUMERS.BUT WE ARE COMMONLY AT A LOSS INTERPRETING ALL THIS NEW INFORMATION. USDA SURVEYS SHOW THAT TWO DECADES AFTER THE RELEASE OF THIS TECHNOLOGY, A MAJORITY OF FARMERS ARE NOT USING A VARIABLE APPROACH (LOWENBERG-DEBOER AND ERICKSON, 2019). THIS SHOWS THAT WHILE INFORMATION IS OVERFLOWING, THE ABILITY TO CAPITALIZE ON THAT INFORMATION IS COMMONLY LACKING. FIELD ENVIRONMENTS ARE MUCH MORE COMPLICATED THAN OFTEN PERCEIVED, AS CROP PRODUCTION IS THE OUTCOME OF DOZENS OF FACTORS. FACTORS THAT CHANGE ACROSS FIELDS AND FROM YEAR TO YEAR, AND FACTORS THAT INTERACT. IN A CROP FIELD, THESE FACTORS CAN INCLUDE SOIL, RAINFALL, TEMPERATURE, PEST INFESTATIONS, CROP GENETICS, PLANTING DATE, AND FERTILIZER APPLICATIONS.THOSE WHO MANAGE CROP FIELDS ARE RESPONSIBLE FOR ANALYZING AND ACTING UPON THE COMPLEXITY OF PRODUCTION ENVIRONMENTS. FARMERS DEPEND HEAVILY ON INPUT SUPPLIERS FOR DECISIONS RELATED TO CROP PRODUCTION. BUT A 2018 SURVEY OF AGRICULTURAL RETAILERS (FAUSTI ET AL, 2018) SHOWED THAT FOR FIVE OF THEIR KEY WORK ROLES RELATED TO TECHNOLOGY, OVER 90% OF RETAILERS INDICATED IT WAS AT LEAST SOMEWHAT DIFFICULT TO FIND A QUALIFIED APPLICANT. WELL OVER HALF OF AGRICULTURAL RETAILERS REPORTED THAT IT WAS EITHER DIFFICULT (TWO TO THREE MONTHS TO FIND A QUALIFIED APPLICANT), VERY DIFFICULT (MORE THAN THREE MONTHS TO FIND A QUALIFIED APPLICANT), OR THERE WERE NO QUALIFIED APPLICANTS IN THEIR AREA. THIS VARIED SOMEWHAT BY THE POSITIONS OF EQUIPMENT OPERATOR, AGRONOMIST, PRECISION EQUIPMENT TECHNICIAN, TECHNICAL SUPPORT, OR PRECISION SALES SPECIALIST, WHICH ARE ALL KEY ROLES IN DIGITAL AGRICULTURE IMPLEMENTATION AT THE FARM LEVEL. AS ANOTHER MEASURE OF THE WORKFORCE, RETAILERS WERE ASKED (IN THE SAME 2018 SURVEY) TO EVALUATE THE SKILLS OF THEIR PROSPECTIVE EMPLOYEES FROM THEIR RECENT INTERVIEWS. WITH THE EXCEPTION OF ONE SKILL, MOST RESPONDENTS INDICATED THEIR INTERVIEWEES WERE EITHER LOW OR DEFICIENT IN EACH OF THE TEN SKILLS MEASURED. INTERVIEWEES HAD THE HIGHEST SKILL LEVELS FOR THEIR ABILITY TO OPERATE PRECISION AGRICULTURE EQUIPMENT; THE LOWEST SKILL LEVEL INDICATED WAS FOR THEIR WORKING UNDERSTANDING OF STATISTICAL STANDARDS TO PRODUCE MEANS AND STANDARD DEVIATIONS, A KEY PART OF DIGITAL AGRICULTURE. UNTIL ARTIFICIAL INTELLIGENCE CAN HELP AUTOMATE INSIGHTS TO BETTER MANAGE PESTS, NUTRIENTS, SOIL, FOOD SAFETY, AND QUALITY, WE WILL NEED HUMAN KNOWLEDGE TO ADDRESS ALL OF THESE AREAS.RETAILERS INDICATE A TWO-YEAR DEGREE IS SUFFICIENT EDUCATION FOR MANY OF THEIR WORK ROLES RELATED TO DIGITAL AGRICULTURE, WHILE BACHELOR'S DEGREE IS DESIRED FOR THE FIELD OF AGRONOMY (ERICKSON ET AL., 2018). AS SUCH, HIGH SCHOOL AGRICULTURE INSTRUCTION IS A KEY ASPECT OF THE KNOWLEDGE PIPELINE FOR AGRICULTURAL RETAILERS. PREVIOUSLY REFERRED TO AS VOCATIONAL AGRICULTURE TEACHERS, SCHOOL-BASED AGRICULTURAL EDUCATION (SBAE) INSTRUCTORS IN HIGH SCHOOLS AND MIDDLE SCHOOLS DELIVER INSTRUCTION IN AGRICULTURE THAT IS CAREER AND TECHNICAL EDUCATION (CTE). CAREER AND TECHNICAL EDUCATION SEEKS TO EQUIP STUDENTS TO BE BOTH COLLEGE- AND CAREER-READY BY HELPING THEM DEVELOP THEIR ACADEMIC, EMPLOYABILITY, AND TECHNICAL SKILLS (ACTE, 2019). AGRICULTURE TEACHERS WORK WITH NUMEROUS STUDENTS AND TEACH MULTIPLE CLASSES IN AGRICULTURE. THE FIELD OF DIGITAL AGRICULTURE CHANGES RAPIDLY, WHICH MEANS A WELL-INFORMED TEACHER FIVE YEARS AGO MAY BE QUITE OUT OF DATE TODAY. KEEPING UP WITH TECHNOLOGY CHANGES IN THE AGRICULTURE INDUSTRY IS A KEY ELEMENT TO EFFECTIVELY PROVIDING OPPORTUNITIES FOR STUDENTS TO LEARN AND PRACTICE SKILLS RELATED TO CURRENT BEST PRACTICES AND HELPING PREPARE STUDENTS FOR CAREERS IN DIGITAL AGRICULTURE.DUE TO THE SPECIFIC NEEDS OF SBAE TEACHERS, WE PROPOSE AN ONLINE COURSE IN DIGITAL AGRICULTURE FOR HIGH SCHOOL AG INSTRUCTORS THAT COULD QUICKLY AND EFFICIENTLY RAMP UP AGRICULTURAL KNOWLEDGE--EDUCATION FOR EDUCATORS. THESE EDUCATORS COULD ALSO USE THE RESOURCES IN THEIR OWN CLASSROOMS. THE SLIDES CREATED FOR THE ONLINE COURSE WOULDBE DOWNLOADABLE, AS WOULD THE SCRIPTS FOR THE DELIVERY AND QUIZ QUESTIONS. THE COURSE CONTENT WILL COMBINE SCIENCE DELIVERED BY SUBJECT MATTER EXPERTS AND APPLICATION DEMONSTRATED BY AGRICULTURAL INPUT SUPPLIERS AND FARMERS. IN ADDITION, WE WILL PROVIDE EXAMPLES TO INTEGRATE THESE SKILLS AND KNOWLEDGE TO STUDENTS WITHIN THE COURSES THAT OUR TEACHER PARTICIPANTS TEACH AT THE SECONDARY LEVEL.
$300,000Purdue University · · FY2021 · National Institute of Food and Agriculture
Johns Hopkins Center of Excellence in Regulatory Science and Innovation
$300,000G Caleb Alexander · Johns Hopkins University · U01 · FY2024 · FD
EAGER: HNDS-I: Next steps in acoustic phonetics research
$300,000Keith A Johnson · University Of California-Berkeley · · FY2025 · SBE