Víctor Guerra
Deep Learning & Machine Learning at Nemotec
A Coruña, Galicia, Spain
Summary
👤
Senior🎓
Top SchoolVíctor Guerra is a Ph.D.-trained computer vision researcher and engineer with nine years of experience applying deep learning to camera pose estimation, keypoint matching and local descriptor design. Based in A Coruña, he blends academic rigor from Universidad de Córdoba with industry practice at Nemotec and prior roles in augmented reality and R&D, shipping vision systems for image analysis and AR. His work spans signal processing to convolutional networks, with a track record of moving methods from research prototypes to production-relevant solutions. Notably, he combines hands-on model development with domain expertise in fiducial markers and practical pose pipelines, making him adept at solving both theoretical and implementation challenges.
9 years of coding experience
4 years of employment as a software developer
Ph.D. in Computer Science, Computer Vision, Ph.D. in Computer Science, Computer Vision at Universidad de CórdobaB. Sc., Technical Engineering in Computer Systems, B. Sc., Technical Engineering in Computer Systems at University of Córdoba
Spanish, English
Stackoverflow
126reputation
63kreached
3answers
0questions
Github Skills (29)
python10
artificial-neural-networks10
classifiers10
scikit-learn10
support-vector-machine10
simulator9
classification9
svm9
machine-learning9
database9
unreal8
artificial-intelligence7
ai7
platform-independent7
control-systems7
Programming languages (2)
Github contributions (5)
mondejar/ecg-prediction-module
Jun 2017 - Mar 2018
Given an ML-II (derivation II) ECG signal, this module detects its beat and returns a class prediction for each one.
Contributions:35 commits, 30 pushes, 1 branch in 8 months
machine-learningecg-signalclassificationsvm
mondejar/ecg-classification
May 2017 - Feb 2019
Code for training and test machine learning classifiers on MIT-BIH Arrhyhtmia database
Contributions:60 commits, 57 pushes, 1 branch in 1 year 9 months
classifiersdatabasemachine-learningmit-bih-arrhythmiaartificial-neural-networks
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