CARLOS FELIPE NAVARRO CLAVERIA

Summary

Applied AI Engineer and Electrical Engineer with 15+ years of experience developing machine learning, computer vision, and AI systems across industrial automation, mining, healthcare, and research environments.

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.

Selected Research & Engineering Contributions

Peer-reviewed work in applied machine learning and computer vision systems:

• Classical ML systems for industrial vision applications Color–Texture Pattern Classification Using Global–Local Feature Extraction with SVM Ensemble Methods Applied Sciences (2019)

• Biomedical signal processing for early detection of neurodegenerative disease. Cochlear dysfunction as an early biomarker of cognitive decline in normal hearing and mild hearing loss Alzheimer’s & Dementia (2024)

• Deep learning systems for 3D microscopy and biomedical imaging VolumePeeler: a novel FIJI plugin for geometric tissue peeling to improve visualization and quantification of 3D image stacks BMC Bioinformatics (2023)

• Vision Transformer models for biomedical image classification RaViTT: Random Vision Transformer Tokens for Biomedical Image Analysis arXiv (2023)

• Computer vision systems for biomedical segmentation 3D Nuclei Segmentation through Deep Learning IEEE CAI (2023)

Full list with links: https://carlosnavarroc.github.io/publications/

Selected Research & Engineering Contributions

Experience

Data Scientist — University of Chile, Faculty of Medicine

2019 – Present

• Development of AI systems for biomedical engineering and applied research.
• Machine learning using physiological signals, medical images, and multimodal datasets.
• End-to-end deep learning pipelines, scientific software, and data analysis.

Applied Research Engineer — University of Chile

2009 – 2018

• Computer vision systems for mining and industrial automation.
• Machine learning for pattern recognition, quality inspection, and process monitoring.
• IoT and sensing systems for real-time industrial monitoring.

Education

PhD in Electrical Engineering — University of Chile (2020)
Diploma in Electrophysiology — University of Chile (2019)
Electrical Engineer — University of Chile (2010)

Technical Skills

Python · R · MATLAB
PyTorch · TensorFlow · Scikit-learn · OpenCV
Machine Learning · Computer Vision · Industrial AI · Signal Processing

Teaching

University teaching experience since 2005. Instructor of record since 2015. 70+ course offerings · 2,500+ students
Undergraduate supervision in AI, Computer Vision, and Engineering.

Professional Activities

Reviewer (ISICS 2024) · Thesis committee member · Speaker in AI and research events

Portfolio

Industrial AI · Computer Vision · Engineering · Professional Timeline · Teaching · Publications
https://carlosnavarroc.github.io