- Evaluated unsupervised machine learning models (K-means, PCA, and Gaussian Mix Models) to classify sweet potatoes based on shape and size with Python.- Developed data management platforms to store heterogeneous agricultural data using Azure Data Lake and Azure Databases.- Developed streaming analytics dashboards using Node.js and React.- Presented research findings in conferences, symposiums, and workshops.

- Wrote academic papers and presented scientific results at national and international conferences.- Evaluated machine learning algorithms to explain grain yield variability in the Southeastern US based on high-dimensional datasets.- Developed and evaluated a weather index-based insurance for soybeans using crop models (DSSAT-CROPGRO-Soybean) and statistical models (PCA, quantile regression, clustering);- Evaluated machine learning models (Random Forest, XGBoost, SVR, Lasso) with Python for yield forecast in the Southeastern US using climate and remote sensing indicators in collaboration with USDA-NASS researchers.

- Developed data pipelines (ETL) to extract, store, and analyze weather station and remote sensing datasets using R and Python.- Developed web and spatial software applications using spatial database (PostGIS) for the visualization of remote sensing indicators. - Evaluated process-based models to simulate soybean, corn, wheat, and cotton development.

- Developed web application with Angular.io, Noje.js, Mongo DB, MySQL, and mobile application for iOS and Android.- Developed Shiny apps and dashboards (HTML, CSS, JavaScript) for data visualization and exploratory analysis.- Developed process-based models using R programming language to simulate the interaction between plant, soil, and atmosphere.