Summary

Data Scientist with 15+ years of experience applying machine learning, computer vision, and signal processing to real-world problems in healthcare, mining, and industry.

I specialize in building end-to-end AI systems, from data acquisition and modeling to deployment and real-world validation, with a strong focus on translating research into practical, robust solutions.

Core Expertise

• End-to-end machine learning pipelines (data → modeling → deployment)
• Deep learning (Transformers, CNNs, biomedical imaging)
• Computer vision systems for real-world environments
• Signal processing and physiological data analysis (EEG, pupilometry)
• Applied AI in healthcare and industrial contexts

Selected Impact

• Achieved top-3 performance (2nd place) in the Cell Tracking Challenge (3D nuclei segmentation)
• Developed machine learning models for glaucoma diagnosis and EEG-based postoperative risk prediction
• Co-developed VolumePeeler, a FIJI plugin for 3D biomedical image visualization (BMC Bioinformatics, 2023)
• Contributed to research on early biomarkers of cognitive decline (Alzheimer’s & Dementia, 2024)
• Led and contributed to interdisciplinary AI projects combining clinical, image, and signal data

Experience

Data Scientist — University of Chile (2019–2025)
Applied machine learning, deep learning, and signal processing to biomedical and clinical problems

Applied Research Engineer — University of Chile (2009–2018)
Developed computer vision and automation systems for mining, retail, and industrial applications

Technical Stack

Python · R · MATLAB
PyTorch · TensorFlow · scikit-learn · OpenCV · FIJI/ImageJ
Docker · Google Cloud (Vertex AI) · Huawei Cloud

Links

• Publications
• Professional Logbook
• Google Scholar
• Cloud Skills Boost Profile

Focus

I work at the intersection of research and real-world deployment, building AI systems that are not only accurate, but also interpretable, robust, and usable in practice.