Sebastian Rueda-Parra zevaz13

👋 Hi, I'm Sebastian

🔬 Bioengineering background with a focus on neuroengineering & rehabilitation research
📊 Passionate about data science, AI, machine learning, and statistics
🧠 Interested in building technology that improves people outcomes
⚙️ Experience working with signal processing, experimental data, Agentic Frameworks, and ML pipelines
🌱 Currently exploring: Color perception. Does color vision tell us something about neurological conditions?
🎹 I love playing piano

🎓 PhD Electrical Engineering — [University of Idaho, 2022]
Focus: Neuroengineering, Data Science, Deep Learning, Robotics
🎓 BSC Electrical Engineering — [Universidad del Valle, 2016]
Focus: Embedded systems, robotics, programming, Computer Vision

Languages:
Python C++ MATLAB

Tools & Libraries:
NumPy Pandas scikit-learn PyTorch Git

Domains:
Machine Learning • Data Analysis • Neuroengineering • Biomedical Systems

🔹 [Neural correlates of bilateral proprioception and adaptation with training] – Investigates how bilateral proprioceptive processing evolves with training, revealing neural signatures of sensorimotor adaptation that inform rehabilitation strategies.
🔹 [Multidimensional feature analysis shows stratification in robotic-motor-training gains based on the level of pre-training motor impairment in stroke] – Uses multidimensional feature extraction and machine learning to uncover distinct recovery profiles, showing that baseline impairment level predicts motor training gains and enabling more personalized rehabilitation approaches.
🔹 [Brain-computer interface (BCI)-based identification of congenital red-green color vision deficiencies] – Addresses the limitations of subjective color vision tests by introducing an objective EEG-based BCI that detects red–green deficiencies through frequency-tagged visual responses.
🔹 [Test-retest reliability of kinematic and EEG low-beta spectral features in a robot-based arm movement task] – Tackles the need for reliable biomarkers by demonstrating stable kinematic and low-beta EEG features across sessions, supporting their use in longitudinal motor assessment and neurorehabilitation studies.