Judie Tabbal

Conducts advanced brain signal analysis and machine learning-based predictive and normative modeling for biomarker discovery and precision medicine, contributing to grant writing and multidisciplinary projects.

Developed computational and neurophysiological biomarkers for behavioral trait modeling and prediction, co-supervised AI projects for neurodegenerative disease diagnosis, and collaborated on secure cloud platforms for brain data analytics.

Assisted in teaching courses in Software Engineering, Artificial Intelligence, and Data Mining at the Department of Computer Science.

Co-supervised three Master's and PhD students in research projects focused on advanced brain signal processing, brain network reconstruction, functional connectome, and motor imagery-based brain-computer interfaces.

Conducted doctoral research on the dynamics of electrophysiological (dys)functional brain networks, focusing on EEG, MEG, brain networks, functional connectivity, and signal processing.

Designed and optimized control algorithms using MATLAB for optical systems, including LED array light-pattern generation for optogenetic stimulation.

Developed a user-friendly web platform to facilitate communication and interaction between parents, schools, and students using HTML, CSS, and JavaScript.

Grade: 88%

Pursued a Master of Science in Technology of Medical and Industrial Systems, with a thesis on Brain-Computer Interface for Smart Living Environment. Achieved a grade of 88% and ranked first in the program.

Grade: 88%

Completed a Master of Science in Biomedical Engineering as part of a co-diplomation with Université Paris-Est Créteil. The thesis focused on a Brain-Computer Interface for Smart Living Environment.

Grade: 94%

Earned a Bachelor of Engineering in Computer and Telecommunication Engineering, with a final year project grade of 94% and ranked first in the class.

Grade: 88.5%

Completed the Lebanese Baccalaureate in Life Sciences with a grade of 88.5%, ranking 7th in North Lebanon.