Milad Torabi

I am a Data Science master’s student with interests in machine learning, computer vision, software engineering, data analysis, EEG signal processing, and biomedical AI. I am currently completing my thesis at TU Delft as a research intern within the Signal Processing Systems group, and I am in the final year of my MSc at Sapienza University of Rome.

My experience spans machine learning, neural networks, software engineering, data analytics, computer vision, and EEG signal processing. I have worked on the following projects and roles:

• Research Intern at TU Delft, working on machine learning and deep learning methods for epilepsy diagnosis using EEG data during photic stimulation.
• Software Engineering Intern at MindAffect, developing and maintaining a Windows-based audiometry diagnostic application (Python, QML) for EEG-based hearing threshold estimation.
• Research and Data Analyst Intern in a Lithuanian Research Council program at Kaunas Faculty of Vilnius University, Lithuania, developing a data-driven framework to assess circular economy performance across EU member states.
• Data Analyst in the telecommunications industry, focusing on automating HR data processing and reporting workflows.

I am also interested in research opportunities involving AI applications in healthcare, neuroscience, and engineering domains such as smart cities.

My current work focuses on automated epilepsy diagnosis using EEG data during photic stimulation. I am particularly interested in applying artificial intelligence and data-driven approaches to solve problems in healthcare and neuroscience.