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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services


Mennatullah Siam
BSc, MSc, PhD (Alberta)

Assistant Professor

Department of Electrical, Computer and Software Engineering

Faculty of Engineering and Applied Science

Contact information

2000 Simcoe Street North
Oshawa, Ontario L1G 0C5
Office: SIRC 3388

905.721.8668 ext. 5743
905.721.3370 (fax)

Research topics

  • Computer Vision
  • Deep Learning
  • Video Object Segmentation
  • Video Understanding
  • Few-shot Learning
  • Interpretability


  • PhD in Computing Science, University of Alberta, AB, Canada, 2021;
  • MSc in Communication and Information Technology, Nile University, Giza, Egypt, 2013;
  • BSc in Computer Science, Ain Shams University, Cairo, Egypt, 2010.


  • Discrete Mathematics for Engineers
  • Data Structures
  • Machine Learning and Data Mining

Research and expertise

Publications and presentations

  • Selected publications and presentations


    • Rezaul Karim, He Zhao, Richard P. Wildes, and Mennatullah Siam, “MED-VT: Multiscale encoder-decoder video transformer with application to object segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
    • Mennatullah Siam*, Naren Doraiswamy*, Boris N. Oreshkin*, Hengshuai Yao, and Martin Jagersand, “Weakly supervised few-shot object segmentation using co- attention with visual and semantic embeddings,” in Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020, pp. 860–867.
    • Mennatullah Siam, Boris N. Oreshkin, and Martin Jagersand, “AMP: Adaptive masked proxies for few-shot segmentation,” in Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 5249–5258.
    • Mennatullah Siam, Chen Jiang, Steven Lu, Laura Petrich, Mahmoud Gamal, Mohamed Elhoseiny, and Martin Jagersand, “Video object segmentation using teacher- student adaptation in a human robot interaction (HRI) setting,” in Proceedings of the International Conference on Robotics and Automation, 2019, pp. 50–56.
    • Masood Dehghan*, Zichen Zhang*, Mennatullah Siam*, Jun Jin, Laura Petrich, and Martin Jagersand (* equally contributing), “Online object and task learning via human robot interaction,” in Proceedings of the International Conference on Robotics and Automation, 2019, pp. 2132–2138.
    • Mennatullah Siam, Sara Eikerdawy, Mostafa Gamal, Moemen Abdel-Razek, Martin Jagersand, and Hong Zhang, “Real-time segmentation with appearance, motion and geometry,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018, pp. 5793–5800.
    • Mennatullah Siam*, Mostafa Gamal*, Moemen Abdel-Razek*, Martin Jagersand, and Senthil Yogamani, “RTSeg: Real-time semantic segmentation comparative study,” Proceedings of the IEEE International Conference on Image Processing, 2018.
    • Mennatullah Siam, Heba Mahgoub, Mohamed Zahran, Senthil Yogamani, Martin Jagersand, and Ahmad El-Sallab, “MODNet: Moving object detection network with motion and appearance for autonomous driving,” Proceedings of the IEEE International Conference on Intelligent Transportation Systems, 2018.
    • Mennatullah Siam, Sara Elkerdawy, Martin Jagersand, and Senthil Yogamani, “Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges,” in Proceedings of the IEEE International Conference on Intelligent Transportation Systems, 2017, pp. 1–8.