Human movement and data science researcher with expertise in machine learning, action classification, motion tracking, and human motion modelling. My doctoral research established a novel framework for quantifying upper limb dexterity, integrating manipulability, workspace, and range of motion analysis. Currently, I apply immersive virtual reality and advanced machine learning techniques to dissect the fundamental components of human threat avoidance behaviour. My work involves developing an unsupervised clustering model to identify fundamental movement motifs (“syllables”) and sequences (“grammar”) to create a comprehensive dictionary of threat avoidance actions, as well as a robust classification system. This research encompasses motion capture (marker-based and marker-less), data curation, feature engineering, dimensionality reduction, and model implementation and evaluation in Python (scikit-learn). My overarching goal is to leverage artificial intelligence to understand complex human movement patterns and address real-world challenges.
Doctor of Philosophy in Mechanical Engineering, 2021
The University of Sheffield
Master of Engineering, 2011
Universidad La Salle Bajio
BEng in Mechanical and Electrical Engineering, 2009
Universidad La Salle Bajio
Python, MATLAB, C#, Version control (GitHub, Gitlab, Bitbucket)
Supervised and unsupervised (e.g., decision trees, random forest, regression, bayes, density-based, hierarchical, and k-means)
Scikit-learn, Pandas, NumPy, Matplotlib
Marker-based (e.g., Vicon Motion Systems, CODA motion), and marker-less (Openpose, DeepLabCut, OpenCV), Action Classification, Motion Modelling
Unity, HTC Vive, Oculus Quest
Spanish (Native), English, German (Basic-Intermediate)
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