Milad Torabi

Machine Learning Researcher & Data Scientist

I am an M.Sc. Data Science candidate with experience in machine learning, statistical modeling, software engineering, data analytics, and signal processing. I am interested in developing reliable and interpretable data-driven solutions for real-world problems across research and industry.

Current Focus

I am currently a Machine Learning Research Intern at Erasmus MC and TU Delft’s Signal Processing Systems group, where I work on interpretable machine-learning methods for epilepsy diagnosis using clinical EEG data. My work includes EEG preprocessing and feature extraction, model evaluation and optimization, reproducible research pipelines, and investigation of epilepsy-related patterns using explainable AI.

Experience

• Machine Learning Research Intern — Erasmus MC & TU Delft
  Developing machine-learning and signal-processing methods for clinical EEG analysis and epilepsy classification.

• Software Engineering Intern — MindAffect
  Developed and maintained a Python/QML audiometry application incorporating EEG-based hearing-threshold estimation.

• Research & Data Analyst Intern — Vilnius University / Lithuanian Research Council
  Developed data-driven methods for comparing circular-economy performance across EU member states.

• Data Analyst — Telecommunication Company of Iran
  Built Python-based automation, analytics, and reporting workflows for operational and HR data.

Interests

My interests include machine learning, data science, biomedical AI, computer vision, and applied AI research. I am particularly interested in opportunities where data-driven methods can contribute to healthcare, neuroscience, engineering, and other complex scientific or industrial problems.