Experienced in deep learning, data analysis, and end-to-end ML pipelines, from data curation and model development to validation and deployment. During my most recent work, I developed deep-learning models for knee localization and multi-class classification, achieving 100% average precision and 70% accuracy, respectively. I also automated full-leg radiograph assessment with landmark-detection and quality-control models, reaching a median localization error of 1.16 mm. My work spans computer vision and medical imaging, including CT, X-rays, and MRI.

Sebastian Amador Sanchez
@sebastianamadorsanch
Computer vision and AI engineer with 5+ years of experience designing, optimizing, and implementing imaging solutions.
Experience
Work history, roles, and key accomplishments
Computer Vision & AI Engineer
Vrije Universiteit Brussel
Sep 2019 - Jun 2026 (6 years 9 months)
o Developed deep-learning models for knee localization and multi-class classification.
o Automated full-leg radiograph assessment using landmark-detection and quality-control models.
o Developed a pose-estimation pipeline for motion analysis from RGB video.
o Built segmentation pipelines for vascular mapping, COVID-19 analysis, and downstream 3D visualization.
Biomedical Engineer
Ángeles del Pedregal Hospital
Sep 2015 - Jul 2017 (1 year 10 months)
Managed medical device installation, maintenance, and regulatory compliance.
Education
Degrees, certifications, and relevant coursework
Vrije Universiteit Brussel
Ph.D. in Engineering, Computer Vision & Deep Learning
2019 - 2026
Grade: Greatest distinction
Activities and societies: Master's thesis mentoring and teaching assistant.
Advancing landmark localization through deep segmentation for reliable malalignment assessment in lower limb radiographs.
Vrije Universiteit Brussel
M.Sc., Biomedical Engineering
2017 - 2019
M.Sc. in Biomedical Engineering from Vrije Universiteit Brussel, completed in 2019.
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
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Social media
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