Manuel Illanes

Designed and implemented end-to-end ETL pipelines using PySpark on Databricks (AWS), processing over 2M daily transactions. Authored and optimized SQL queries for reporting and dashboards, reducing runtime by 40%. Automated data ingestion and created PowerBI dashboards for real-time insights.

Developed the syllabus for the Psychology of Emotions course and initiated the international curriculum validation process. Taught course content focusing on the neurobiological basis of emotions, emotional regulation, and their impact on behavior.

Developed research software for a Psychophysiology lab using Python, Docker, JavaScript, and AWS. Created socket protocols for hardware data integration and used web frameworks for data collection in a MongoDB database. Conducted research on experimental design and statistical analysis of biometric data using Python and R.

Led the development of a self-attention Transformer Network, improving BCI task accuracy by 30%. Developed experimental software using .NET and Unity for model evaluation and integrated with 20+ EEG headsets. Created a visualization tool for brain topography analysis and organized team tasks using Scrum.

Developed a 3D visualization tool for real-time fNIRS data using React Native and Django, improving speed and accuracy by 60%. Created mobile apps with Flutter and Unity for fNIRS data integration and developed behavioral tasks for ADHD assessment. Utilized Python to enhance Neural Network model accuracy for attention estimation by 20%.

Instructed courses on programming and game development using Blender3D and the Unity Engine. Designed the curriculum for 'Unity for Kids' and 'Unity for Teens', teaching over 80 students.

Applied data analysis techniques including time-frequency analysis and multivariate statistics to a BCI prototype. Conducted research on cognitive performance using a focus estimation algorithm. Developed new features for an internal experimental platform using Unity and C#, including data recording and external software integration.

Assisted in teaching a computational neuroscience course covering Machine Learning, Deep Learning, and theoretical neuroscience models. Utilized Python frameworks including PyTorch, Scikit-Learn, Numpy, and Scipy for structuring classes.

Completed a Master of Science program focusing on Data Science principles and applications. Studied advanced topics in data analysis, machine learning, and statistical modeling.

Grade: Magna Cum Laude

Pursued a Bachelor's degree in Psychology, covering core areas of psychological science. Gained knowledge in research methods, statistical analysis, and various branches of psychology.