Sinisa Colic

Sinisa Colic is an Assistant Professor, Teaching Stream, in the Department of Mechanical & Industrial Engineering at the University of Toronto. His research areas include Applied Machine Learning, with specific interests in Mechatronics, Automation, and the Applications of Deep Learning. Dr. Colic's teaching portfolio spans courses in data science, deep learning, and mechatronics, emphasizing practical and interdisciplinary engineering applications. He earned his Ph.D. in Electrical and Computer Engineering from the University of Toronto and has contributed to innovation and entrepreneurship through projects involving EEG-based systems and digital twin technologies. His current academic interests focus on integrating Industry 4.0 technologies into engineering education and promoting student engagement through robotics clubs and competitions.

Sinisa Colic is an applied machine learning researcher at the University of Toronto, focusing on the application of machine learning techniques to neuroscience and clinical data analysis. His work includes the development of predictive models for antiepileptic drug treatment outcomes, the identification of brain regions for epilepsy surgery planning, and the use of deep learning algorithms to distinguish psychiatric disorders from healthy controls using resting EEG. Colic also explores the use of machine learning in the management of suicide ideation and the characterization of seizure-like events in mouse models of neurological disorders. His research bridges computational methods with biomedical applications, aiming to improve diagnostic and therapeutic approaches in neurology and psychiatry.