Thainara Lima is a PhD student in Agricultural and Biosystems Engineering at Mississippi State University and a NASA Early Career Research Fellow. Her PhD project develops scalable AI pipelines for large-scale geospatial data on HPC/GPU infrastructure, with a focus on global algal bloom detection in coastal waters. She explores Vision Transformers, semantic segmentation, and self-supervised learning for Earth observation, while advancing domain-aware foundational models. Her broader research interests span AI-driven innovation, multimodal learning, and sustainable monitoring solutions that integrate satellite data, computer vision, and high-performance computing for planetary-scale environmental challenges. She holds a master’s degree in Remote Sensing from the Brazilian Institute for Space Research (INPE), Brazil, where she worked on calibrating bio-optical and machine learning models using satellite data. She earned her bachelor’s degree in Geomatics and Surveying Engineering, with an internship at the University of New Brunswick in Canada, with a focus on GNSS, precision agriculture, and remote sensing. She possesses extensive experience in mapping and monitoring agricultural crops in Brazil using Earth Observation (EO) and deep learning, building independent, scalable pipelines that integrate data inges tion, advanced augmentation, and active learning loops. Thainara has collaborated with research groups at the National Research Council of Italy (CNR-IREA) and the Instrumentation Laboratory for Aquatic Systems (LabISA), applying hyperspectral analysis and deep learning to complex aquatic and terrestrial environments.