CARLOS FELIPE NAVARRO CLAVERIA

Summary

Applied AI Engineer and Electrical Engineer with 15+ years of experience designing AI systems for industrial automation, mining, biomedical engineering, and scientific research, spanning computer vision, machine learning, sensing, deployment, and scientific software.

Experienced in designing end-to-end AI systems spanning data acquisition, sensing, modeling, deployment, and validation, with a focus on solving real-world engineering problems.

Professional Experience

Applied AI Engineer / Data Scientist — Faculty of Medicine, University of Chile

2019 – 2026

• Designed deep learning pipelines for biomedical imaging, microscopy, and physiological signal analysis.
• Developed computer vision models for segmentation, classification, and quantitative image analysis.
• Built end-to-end machine learning workflows from data acquisition through validation.
• Developed scientific software supporting reproducible biomedical research.
• Collaborated with multidisciplinary clinical and engineering teams.

Applied Research Engineer — University of Chile

2009 – 2018

• Developed computer vision systems for industrial inspection, mining, and automation.
• Designed machine learning algorithms for pattern recognition and texture classification.
• Built image acquisition, sensing, and embedded monitoring systems.
• Developed IoT solutions for industrial environments.
• Executed R&D projects from concept through prototype validation.

Technical Skills

Languages: Python · MATLAB · R

Frameworks: PyTorch · TensorFlow · Scikit-learn · OpenCV

Areas: Computer Vision · Machine Learning · Deep Learning · Signal Processing · Industrial AI · Biomedical AI

Selected Engineering Projects

Mining Vision Systems — Developed computer vision solutions for conveyor belt monitoring, automated lithology classification, and industrial process inspection in mining operations.

Industrial Sensing — Designed sensing and monitoring systems for industrial environments, including Bluetooth-based instrumentation for slope stability monitoring in Canadian mining operations.

VolumePeeler — Developed open-source software for geometric tissue peeling, enabling quantitative analysis of complex multilayer 3D microscopy datasets in vertebrate, invertebrate, and plant models.

3D Biomedical Imaging — Developed deep learning methods for nuclei segmentation and quantitative analysis of embryonic development, including epiboly and morphogenesis in annual killifish.

AI Biomarkers — Developed machine learning models using EEG and cochlear physiological signals for early detection of cognitive decline and neurodegenerative disease biomarkers.

Education

PhD, Electrical Engineering — University of Chile (2020)

Diploma, Electrophysiology — University of Chile (2019)

Electrical Engineer — University of Chile (2010)

Teaching

University instructor since 2015 • 70+ courses • 2,500+ students • Undergraduate supervision

Professional Activities

Reviewer • Thesis committee member • Invited speaker

Selected Publications

• Alzheimer’s & Dementia (2024)
• BMC Bioinformatics (2023)
• IEEE CAI (2023)
• Applied Sciences (2019)

Publications: https://carlosnavarroc.github.io/publications/

Portfolio: https://carlosnavarroc.github.io