PhD, P.Eng.
Assistant Professor, Teaching Stream, Mechanical Engineering
Email: colic@mie.utoronto.ca
Tel: 416-978-5435
Office: MC314
Research Areas
Applied Machine Learning
Research Interests
Mechatronics; Automation; Applications of Deep Learning
Bio
Dr. Colic is an Assistant Professor, Teaching Stream, in the Department of Mechanical and Industrial Engineering at the University of Toronto. His teaching portfolio spans courses in data science, deep learning, and mechatronics, with a strong emphasis on practical and interdisciplinary engineering applications. He earned his Ph.D. in Electrical and Computer Engineering from the University of Toronto, where his research focused on time-series analysis, signal processing, and machine learning for the development of data-driven treatment strategies in healthcare. Following his doctoral studies, Dr. Colic completed a postdoctoral fellowship at McMaster University, working with medical imaging data to support the diagnosis and treatment of mood disorders.
Dr. Colic received the Best Paper Award at the 35th Annual Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in Osaka, Japan, for his work on cross-frequency coupling as a tool to characterize seizure-like events. He has been an active contributor to innovation and entrepreneurship, Dr. Colic was part of the team that won the Digitech Innovation Prize in Paris, France, for developing an EEG-based system to manage major depressive disorder. He is a co-founder of With3D Inc., a consulting firm that has delivered services to both early-stage startups and established suppliers in the automotive and manufacturing industries.
With over a decade of involvement with the University of Toronto Engineering Outreach office, Dr. Colic has played a key role in designing and implementing experiential learning techniques in engineering education. He was the recipient of the MIE Early Career Teaching Award in recognition of his outstanding contributions to engineering education. His current academic and research interests are centered around three main areas:
(i) the development of hands-on, industry-informed learning experiences in engineering education, including the integration of Industry 4.0 technologies into the curriculum;
(ii) advisory roles on interdisciplinary projects at the intersection of deep learning, mechatronics, and robotics—such as preventative maintenance, anomaly detection, and digital twin systems;
(iii) promoting student engagement through engineering robotics clubs and competitions.