Coded distributed computation and applications on distributed optimization

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Coded computation is an emerging technique that uses error correction codes to improve the reliability of distributed computation systems. Broadly speaking, the technique involves taking a big computational task and dividing it into smaller subtasks along with additional redundancy.

Coded computation schemes guarantee that the overall computational tasks can be accomplished even if some of the smaller subtasks are not completed.

This PhD project forms part of a cluster collaboration between the University of Toronto and the University of Melbourne titled Communication and Computation: Two sides of one tapestry.

Project goals

The goals of this project are to:

  1. Leverage powerful techniques from information theory, coding theory and machine learning to provide new and improved solutions to coded computation
  2. Investigate the ability to use coded computation to solve large-scale optimization problems, which are often encountered in modern machine learning tasks

Supervision team

*Click on the researcher's name above to learn more about their publication and grant successes.

Who we are looking for

We are seeking a PhD candidate with the following skills:

  • Demonstrated experience in the field of computational engineering, communication systems, information theory and machine learning.
  • Demonstrated ability to work independently and as part of a team
  • Demonstrated time and project management skills
  • Demonstrated ability to write research reports or other publications to a publishable standard (even if not published to date)
  • Excellent written and oral communication skills.
  • Demonstrated organisational skills, time management and ability to work to priorities.
  • Demonstrated problem-solving abilities.
  • The ability to work independently and as a member of a team.

Further details

The PhD candidate will benefit from the combined expertise of the project supervisors, and the embedding into two research environments.

Dr Jingge Zhu and Professor Jamie Evans at the University of Melbourne, and Professor Stark Draper and Professor Wei Yu at the University of Toronto will contribute expertise in information theory, communication theory and machine learning to this project.

This PhD project will be based at the University of Toronto with an 18-24 month stay at the University of Melbourne.

The candidate will be enrolled in the PhD program at the Department of Electrical and Computer Engineering at the University of Toronto, and in the PhD program at the Department of Electrical and Electronic Engineering at the University of Melbourne.

To apply for this joint PhD opportunity, and to view the entry requirements, visit How to apply.

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