Majid Komeili

Majid Komeili is an Associate Professor at Carleton University's School of Computer Science and a faculty affiliate with the Institute for Data Science and Accessibility Institute. He leads the Intelligent Machines Lab (iML), focusing on interpretable machine learning, deep neural networks, and transfer learning. His research spans explainable AI applications in healthcare, accessibility, and social media analysis. Komeili holds a PhD from the University of Toronto's Department of Electrical and Computer Engineering and completed a postdoctoral fellowship at the Vector Institute and Toronto Rehabilitation Institute.

Education

• PhD, Electrical and Computer Engineering, University of Toronto (201X)
• Postdoctoral Fellow, University of Toronto (Vector Institute & Toronto Rehabilitation Institute)

Research Interests

• Interpretable AI and Explainability
• Deep Learning & Generative Models
• Computer Vision & NLP Applications
• Healthcare AI & Accessibility Technologies
• Transfer Learning & Few-Shot Learning

Key Projects

• AI-generated tactile graphics for visually impaired individuals
• Explainable AI for vaccine hesitancy analysis
• Generative models for accessibility in healthcare
• Collaborations with Ottawa Hospital, Accessibility Institute, and Carleton's Law & Journalism departments

Teaching

Recent courses include Advanced Machine Learning (COMP 5801/5900), Introduction to Data Science (DATA 5000), and Computer Vision (COMP 4102). He emphasizes hands-on learning with PyTorch and Python.

Advising

Currently supervises 10+ graduate and undergraduate students, with past advisees moving to roles at Larus Technologies, JSI, and the Canada Revenue Agency. His lab supports diverse research topics from NLP to medical imaging.

Labs & Teams

Directs the Intelligent Machines Lab (iML), collaborating with multidisciplinary teams on AI ethics, healthcare tech, and accessibility innovations.