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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.

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Faisal Qureshi

Faisal Qureshi

Associate Professor

Computer Science Graduate Program Director

Faculty of Science

Exploring theory and models for the next generation of ad hoc networks of smart cameras providing perceptive coverage of extended spaces

Contact information

Science Building - Room UA4071
North Oshawa
2000 Simcoe Street North
Oshawa, ON L1G 0C5

905.721.8668 ext. 3626

Research topics

  • Computer Vision
  • Camera Networks
  • Computer Graphics
  • Smart Graphics
  • Behaviour-based Computer Animation
  • Cognitive Vision
  • Video Surveillance
  • Autonomous Characters for Computer Animation and Games
  • Autonomous Agent Architectures

Areas of expertise

  • Computer Vision
  • Smart Camera Networks
  • Deep Architectures for Computer Vision
  • Graphics for Vision


A long daily commute for millions of drivers fuels serious health and economic consequences. Vehicle emissions are the leading cause of air pollution in North America; and idling for long periods in traffic each day significantly reduces productivity. In an effort to curb traffic congestion in urban centres, Faisal Qureshi, PhD, Associate Professor of Computer Science, in the Faculty of Science, is developing new techniques to capture and analyze big data involved in traffic camera networks, to better understand traffic patterns and conditions. Using that data, he is developing sensors for next generation smart camera networks to detect incidents, divert vehicles and improve traffic flow in urban centres and on highways. This emerging technology will make more intelligent use of existing road networks, help reduce air pollution, and ultimately mitigate climate change. His research also has meaningful benefits for older adults who want to live independently at home for as long as possible. Dr. Qureshi is creating smart camera networks to allow family members to monitor elderly relatives, particularly those with dementia. To facilitate smart homes, he is developing sensors that family members could program to give cues to the user, reminding them to complete important daily tasks, and helping them maintain independence. A computer science expert, Dr. Qureshi is fascinated by understanding how human intelligence works and using it to build novel systems capable of carrying out complex tasks without human intervention. He earned his Bachelor of Science in Mathematics with a Minor in Physics from Punjab University in Lahore, Pakistan in 1992, and his Master of Science in Electronics from Quaid-e-Azam University in Islamabad, Pakistan in 1995. Dr. Qureshi joined Ontario Tech in July 2008 as an assistant professor. Instrumental in establishing the university’s Visual Computing (VC) Lab, he was named associated professor in July 2013. He gained industry experience as a software engineer with Toronto-based Autodesk Canada Co., and as a contract engineer with MDRobotics in Brampton, Ontario.


  • PhD University of Toronto 2007

Research and expertise

Dr. Qureshi’s scientific and engineering interests center on the study and development of computational models of visual perception that underpin autonomous, purposeful behavior in the context of autonomous vehicles, self-organizing sensor networks, unmanned space robotics, and intelligent virtual characters. In his intellectual pursuits, he is inspired by the amazing biological systems around us, which are capable of sustained, unsupervised, sophisticated behavior in a world that is notoriously difficult to model. The Grand Promise of our field, he says, is systems that are smart enough to perform their tasks reliably with minimal or no human supervision. He finds the challenge of contributing to the development of such systems exhilarating.

Building smart autonomous systems capable of purposeful behavior in unpredictable, dynamic environments that defy complete and accurate modeling is a formidable problem, one whose solution rests upon efficient, tractable, and robust computational models of synthetic intelligence, persistent perception, and life-like motor skills. He is interested in all these facets of a smart system, be it perception, motor control, or decision making. Each is a difficult and interesting sub-problem in its own right, but thus far his focus has been on models and systems for visual perception to solve challenging problems in camera sensor networks, space robotics, low-level computer vision, vision streams, road traffic analysis, and attention.

Dr. Qureshi joined Ontario Tech University in 2008 where he established the Visual Computing Lab (VCLab). VCLab focuses on problems residing at the intersection of computer vision, visual sensor networks, large-scale processing and computer graphics. Dr. Qureshi’s work has made significant strides towards the development of ad hoc networks of smart cameras capable of visual perception of extended public spaces. Dr. Qureshi’s work on intelligent perception for space robotics was used by MDA Corporation to develop a first of its kind in-orbit satellite capturing manipulator. This work led MDA to support Boeing’s team win the 12 million dollar Orbital Express contract.


  • Selected publications
    • “A Residual-Dyad Encoder Discriminator Network for Remote Sensing Image Matching,” Khurshid, N.; Mohbat; Taj, M.; and Qureshi, F. IEEE Transactions on Geoscience and Remote Sensing, 14pp., 2019. In press
    • “Joint Spatial and Layer Attention for Convolutional Networks,” Joseph, T.; Derpanis, K.; and Qureshi, F. In 30th British Machine Vision Conference (BMVC19), 14pp, Cardiff, Sep 2019.
    • “Neural Networks Trained to Solve Differential Equations Learn General Representations,” Magill, M.; Qureshi, F.; and de Haan, H. In The Thirty-second Annual Conference on Neural Information Processing Systems (NuerIPS 18), 11pp, Montreal, Dec 2018.
    • “Fast Estimation of Large Displacement Optical Flow Using Dominant Motion Patterns & Sub-Volume PatchMatch Filtering,” Helala, M.; and Qureshi, F. In 14th Conference on Computer and Robot Vision (CRV 17), 8pp, Edmonton, May 2017. Best Computer Vision Paper
    • “Stereo Reconstruction of Droplet Flight Trajectories,” L.A. Zarrabitia; F.Z. Qureshi; and D.A. Aruliah. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4), April, 2015, 847–861.
    • “Smart Camera Networks in Virtual Reality,” F.Z. Qureshi; and D. Terzopoulos. Proceedings of the IEEE, 96(10), October, 2008, 1640–1656, (Special Issue on “Smart Cameras”).
    • “Intelligent Perception and Control for Space Robotics: Autonomous Satellite Rendezvous and Docking,” F.Z. Qureshi; and D. Terzopoulos. Journal of Machine Vision Applications, 19(3), February, 2008, 141–161.

Current Projects

  • Remote sensing image analysis
    Joint work with The Remote Sensing and Spatial Ecosystem Modeling Lab, University of Toronto and Computer Vision Lab, Lahore University of Management Sciences
  • Bird flight estimation in wind farms
    Joint work with Mid Sweden University
  • Model-agnostic-meta-learning for image classification
  • Single image human pose estimation
  • Theory and models for deep computational attention
  • Cross-view action recognition
  • Artificial Intelligence driven tools for analyzing plant systems ‘omics data Joint work with Department of Cell & Systems Biology, University of Toronto and other