Jordan shapes for deep learning - ON-147

Preferred Disciplines: Deep Learning, Machine Learning, AI, Computer Science (Masters; PhD or Post-Doc)
Company: ShapeVision Inc.
Project Length:  4-6 months (1 unit)
Desired start date: As soon as possible; ideally no later than September 1, 2018
Location: Ottawa, ON
No. of Positions: 1
Preferences: University of Toronto, including Vector Institute, and University of Montreal, including MILA.

About the Company: 

ShapeVision Inc, an Ottawa R&D startup, applies Jordan shape analysis to medical images, computer vision, and photography. Founder & CTO Dr. Martin Brooks describes ShapeVision as an opportunity to leverage diverse image representations. ShapeVision will provide the project with a proprietary web service to transform pixels to shapes.

Project Description:

Project objectives:

  • List & prioritize all the methods by which neural net training & inference might leverage simultaneous use of two image representations, pixels and Jordan shapes.
  • Implement & evaluate at least three of the most promising methods.

Research description:

  • A Jordan shape is represented as a circular sequence of points, in the continuous image plane, defining the shape’s outer perimeter, including also a circular point sequence for each of the shape’s holes.
  • ShapeVision software will be used to transform pixel images into hierarchies of Jordan shapes.
  • Shape data may be used in conjunction with pixel data to segment and identify objects in the scene.
  • Shape data may be used in one or both training and inference.
  • In some experiments, shape data may be used in the loss function, or as input, or for explanation.
  • Research results will provide an overview and in-depth analysis of how shape data may impact deep learning training, inference and transparency.

Research Objectives:

  • Create and disseminate knowledge of how deep learning training & inference can leverage multiple, diverse data representations.
  • In particular, determine if & how Jordan shape representation of 2-dimensional images can best be used with deep learning for instance segmentation and scene understanding.


ShapeVision will manage the project in four phases of full-time intern work. The intern will work closely with Dr. Martin Brooks, who will be available at least 1.5 full days each week. Collaboration will include site visits and videoconference.

  1. Planning phase: Choose: shape integration strategies for subsequent experiments, datasets to be used in the experiments, and compute platforms for each experiment; and specify data collection, evaluation, and reproducibility requirements for each experiment.
  2. Experiment phase: For each chosen shape experiment, use three part protocol: (1) hack & train; (2) proof of reproducibility; (3) formal test and data collection.
  3. Evaluation phase: Summarize data, with discussion of possible conclusions; and, self-assessment of project strengths and weaknesses.
  4. Dissemination phase: Reproducibility resources made publicly available; intern-written code made available as open source.

Post-project: Academic publication by the intern.

Expertise and Skills Needed:

    • Knowledge of deep learning principles, tools, and datasets for computer vision.
    • Experience training deep learning on multi-GPU platforms, including pre-trained models.
    • Ability to code at all levels of the deep learning stack, including extensions to deep learning models (e.g. new loss function), new network structures (e.g. injecting shape data into a middle layer), and integration with external processes (e.g. request shape image; or, human in the loop).

    For more info or to apply to this applied research position, please

    1. Check your eligibility and find more information about open projects
    2. Interested students need to get the approval from their supervisor and send their CV along with a link to their supervisor’s university webpage by applying through the webform or directly to Nadia Dubé at ndube(at) or to Iman Yahyaie at iyahyaie(at)