I am Jodavid Ferreira, Professor in the Department of Statistics (DEst) at the Federal University of Pernambuco (UFPE), Brazil.
Academic Background
- Postdoctoral Fellowship in Decision Models and Health (PPGMDS) — Federal University of Paraíba (UFPB)
- Ph.D. in Statistics — Federal University of Pernambuco (UFPE)
- M.Sc. in Statistics — Federal University of Pernambuco (UFPE)
- B.Sc. in Statistics — Federal University of Paraíba (UFPB)
Research Agenda
In my academic trajectory, I actively develop research in statistical learning, supervised classification, and unsupervised clustering/segmentation. My theoretical work spans:
- Matrix-variate probability distributions & statistical inference
- Hypothesis testing & information-theoretic distance measures
- Polarimetric Synthetic Aperture Radar (PolSAR) remote sensing image processing
- Vision Transformers (ViT) & deep representation learning
Research Laboratories & Affiliations
I actively contribute to and hold leadership roles in specialized academic research laboratories across UFPE and UFPB:
- LMET-SR — Laboratório de Modelagem Espaço-Temporal para Sensoriamento Remoto (UFPE) · Vice-Coordinator
- CASTLAB — Computational Agriculture Statistics Laboratory (UFPE) · Vice-Coordinator
- LEEME — Laboratório de Estatística Espacial e Modelagem Estatística (UFPE) · Professor & Researcher
- LEAPIG — Laboratório de Estatística Aplicada ao Processamento de Imagens e Geoprocessamento (UFPB) · Professor & Researcher
- LABTEVE — Laboratório de Tecnologias para o Ensino Virtual e Estatística (UFPB) · Professor & Researcher
Open-Source & Computational Science
I am an advocate and enthusiast of Free and Open-Source Software (FOSS). I build computational tools in R and Python, creating packages and reproducible workflows for data science, remote sensing, and statistical modeling.
Industry Experience & AI Leadership
Alongside academic appointments, I have served as Technical Lead of Data Science & Artificial Intelligence in Brazilian technology startups:
- Generative AI & LLM Systems: Architected production solutions utilizing foundation models (GPT-4, Gemini), Retrieval-Augmented Generation (RAG), and fine-tuning pipelines.
- Enterprise Data Science: Developed machine learning pipelines for predictive scoring, persona profiling with statistical metrics, and customer recommendation engines.