*Updates for 2024 are in progress.

 

Supervisors  

Amirali Salehi-Abari | Ana Duff | Bill Kapralos | Julie Thorpe | Khalil El-Khatib | Loutfouz Zaman | Salma Karray | Samaneh Mazaheri | Shahram S. Heydari| Stephen Marsh

 

Supervisor name: Amirali Salehi-Abari

Project title: Graph Neural Networks for Recommender Systems

Summary of research project: Recommender systems are prevalent in our day-to-day life. They intelligently recommend desirable options to us (e.g., books in Amazon, movies in Netflix, etc.), which are consistent to our own taste or preferences.  Several recent developments have been made in applying deep learning in recommender systems. Of important and promising direction is utilizing graph embedding and graph convolutional networks for empowering recommender systems with graph-structured side information (e.g., social networks). Our goal is to develop effective deep learning algorithms for large-scale recommender systems in the presence of graph-structured side information. 

Student responsibilities/tasks:

• The Student is expected to review the relevant research literature along with other team members.
• The Student also will be heavily involved in development of the AI technologies and will co-author the consequent paper written from his involvement in this Project.
• This Project provides a unique opportunity to the candidate for fostering his/her knowledge in AI, Graph Neural Networks, and Deep Learning.

Student qualifications required:

• The Student is expected to have familiarity with PyTorch and Deep Learning and be excellent in programming in Python.

Expected training/skills to be received by the Student:

• Graph Neural Networks
• Programming in PyG
• Literature Review
• Academic Writing

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR Award

 

Supervisor name: Ana Duff

Project title: Increasing student problem-solving capacity

Summary of research project: This Project will focus on the development of methodology and tools that can be implemented with first year undergraduate students to strengthen their problem-solving skills. Many students enter undergraduate studies with simplistic view and experience in problem-solving and struggle with the complexities they face at the university.

The Project will explore the validity of methodology that focuses on breaking the problem down in a systematic way, following the principle of ‘just-in-time-information’ and aiming to reduce the cost of ‘mental clutter’ in the problem-solving process. The Project will mainly focus on problem-solving in mathematics, but is also applicable in other disciplines.

Student responsibilities/tasks:

• Researches and collects data as directed by Project Supervisor
• Interprets, synthesizes and analyzes data
• Reports on status of research activities; plans and modifies research techniques and procedures;
• Writes and edits materials for reports and presentation
• Meets with Project Supervisor on weekly basis to maintain ongoing communication regarding the quality of Student’s performance
• Performs other related duties as required.

Student qualifications required:

• Demonstrated problem-solving capacity
• Ability to work independently, to apply logical critical analysis and to problem-solve technical and methodological issues arising during research
• Strong organizational, interpersonal and communication skills
• Access to a personal computer, internet connection, microphone and web camera.

Expected training/skills to be received by the Student:

• Data collection: identifying useful data and critically evaluating quality of datasets for errors or problems
• Data management: organizing data, assessing methods to clean data, identifying outliers and anomalies, cleaning data
• Data evaluation: applying data analysis tools and techniques, conducting exploratory analysis, identifying discrepancies within data
• Data presentation: creating meaningful tables and charts to visually present data, converting data to actionable information

Length of award: 14 Weeks

Location of award: Remote

Available Award: Ontario Tech STAR Award

 

Supervisor name: Bill Kapralos

Project title: Immersive Technologies for Medical Education

Summary of research project: Working with an interdisciplinary team of medical professionals, educators, and game developers/computer scientists, this position will involve developing new (or modifying existing) virtual simulations/serious games for medical education. This may involve working with virtual reality, and augmented reality technologies.

Student responsibilities/tasks:

• Develop (or modify) virtual simulation(s)/serious game(s)
• Conduct basic research on specific topics
• Work in an interdisciplinary team

Student qualifications required:

• Prior programming experience
• Prior experience with the Unity Game Engine would be ideal but not required
• 3D modeling and rigging would be ideal but not required

Expected training/skills to be received by the Student:

• Ability to conduct basic background research
• Ability to work with an interdisciplinary team
• Programming experience
• Experience writing research articles/technical reports

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR Award

 

Supervisor name: Julie Thorpe

Project title: Secure Use of Password Managers

Summary of research project: Password managers are considered a useful solution the problem of remembering unique passwords across a multitude of accounts. However, their adoption is not widespread. Recent literature suggests that even when people adopt password managers, they are often not using the password generation features and still reusing some passwords. This Project aims to design and evaluate novel user interface enhancements for password managers, as well as awareness tools to increase secure use of password managers.

Student responsibilities/tasks:

• Designing new user interfaces
• Web programming of prototypes
• Database design
• Writing analysis scripts
• Running user studies to collect data
• Analyzing results
• Writing
• Reading relevant papers
• Participating in meetings

Student qualifications required:

• Programming course (Min. grade of A-)
• Web programming experience or course(s).

Expected training/skills to be received by the Student:

• Software design and development
• Security knowledge
• Research methods

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR Award

 

Supervisor name: Khalil El-Khatib

Project title: Threat Analysis in Connected and Autonomous Vehicles

Summary of research project: Connected and Autonomous Vehicles (CAVs) are disrupting the automotive and transportation industry and introducing rapid technological evolution. The benefits and possibilities with connected and autonomous vehicles are plentiful, however they are not without risk. The speed of innovation and adoption of CAV technologies must be complemented by comprehensive and robust testing to ensure the safety of the vehicles' passengers and data. The objective of this Project is to evaluate the threat landscape with connected and autonomous vehicle and to develop some security testing use cases.

Student responsibilities/tasks:

• The Student will be responsible to work with a team of graduate and undergraduate students working on security and privacy aspects with connected and autonomous vehicles.
• The Student will help with the development of security test cases, help with the setup and execution of these test cases.
• The Student will be involved in the evaluation of the results of the test cases.

Student qualifications required:

• Strong Information security background
• Strong understanding of networking protocols
• Strong analysis skills
• Strong oral and written communication skills

Expected training/skills to be received by the Student:

• Evaluation the threat landscape for connected and autonomous vehicles
• Perform research and security testing
• Analysis of security testing results
• Communication of testing results in conference and journal papers
• Work in a team environment

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR Award

 

Supervisor name: Loutfouz Zaman

Project title: Animal Identification and Emotion Recognition using Deep Learning

Summary of research project: We have previously used deep learning to help expedite the procedure of finding lost cats and dogs. We applied transfer learning methods on different convolutional neural networks for cats and dogs classification and identification. We achieved an accuracy of 98.18% on species classification. In the face identification layer, 80% accuracy was achieved. Body identification resulted in 81% accuracy.

The Student will work on extending one or more aspects of the framework. These are concerned with nose print identification, voice recognition, emotion detection, and extending support for other species.

Student responsibilities/tasks:

• We aim to improve the performance of the framework, by including nose print identification and voice recognition.
• Capsule networks will be used in the system to add more flexibility in the body poses and nose prints.
• Emotion recognition is valuable to identify the stress level of animals that have been found.
• Extending support for more species is also planned.
• The Student will work on extending one or more aspects of the framework.

Student qualifications required:

• Preference will be given to students who are familiar with deep learning, have experience using relevant libraries in Python, and who have worked with the Jupyter environment.

Expected training/skills to be received by the Student:

• Experience solving a real world problem using deep learning, which will be later deployed for public use
• Experience of using Python and libraries like tensorflow and Jupyter, beyond the scope of assignments and course projects.

Length of award: 14 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR Award

 

Supervisor name: Salma Karray

Project title: Effects of advertising on consumer engagement

Summary of research project: This research aims to develop a conceptual framework for understanding and summarizing the marketing literature about consumer online response to advertising. The main objective of this research is to collect research papers about this subject and write a literature review that summarizes research findings to date and identifies future research avenues.

Student responsibilities/tasks:

• Students applying for this project need to have excellent writing and comprehension skills, a curious mind-set and should have successfully completed marketing courses at Ontario Tech or another institution.

Student qualifications required:

• Completed BUSI 2200U (or equivalent) with min grades of A-
• Excellent writing and comprehension skills
• Min grades 3.5

Expected training/skills to be received by the Student:

• Identify main outlets for academic research in marketing
• Identify relevant research articles published in main academic publications.
• Catalog and summarize the collected research articles
• Write a literature review that summarizes the collected papers and identifies future research avenues.

Length of award: 14 Weeks

Location of award: Remote

Available Award: TBD

 

Supervisor name: Samaneh Mazaheri

Project title: Evaluating the usefulness of an Artificial Intelligence (AI) technique to support and improve breast cancer screening practice

Summary of research project: AI is a potential innovative tool to automatically and accurately detect and classify different masses in mammography images.
This study aims to provide evidence for future larger prospective studies to evaluate the ability of a new deep learning algorithm for mass detection in mammograms. You Only Look Once (YOLO) is a recently developed deep learning algorithm that combines object detection and classification tasks into a single step.
By evaluating the ability of the YOLO AI algorithm to accurately perform mass detection, important early information on the usefulness of this technique to support and improve breast cancer screening practice will be obtained.

Student responsibilities/tasks:

• Implementing YOLO algorithm for a publicly available mammography dataset to compare the result with previous research and publish a paper.

Student qualifications required:

• Courses completed: Object-oriented programming + Machine learning + Statistics & Probability
• Skills required: Python programming

Expected training/skills to be received by the Student:

• Medical image processing
• Data analysis
• Writing a scientific report (publication e.g. paper))
• Literature review

Length of award: 14 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR Award

 

Supervisor name: Shahram S. Heydari

Project title: Adversarial Artificial Intelligence Cyber defence Environment

Summary of research project: In this Project, our goal to build a virtual environment to develop and test defence mechanisms against adversarial and polymorphic cyberattacks. Such attacks can change their profiles and features in order to evade detection by IDS systems. In particular, we focus on a number of autoencoder incremental adversarial learning methods that were developed by our team, and we intend to test their performance against adversarial attacks in an isolated, virtual test environment.

Student responsibilities/tasks:

• Developing and testing a polymorphic attack tool based on our WGAN module
• Extending and testing an AI-enabled IDS based on Snort tool.
• Writing programs and scripts (Primarily in Python) to connect different modules of the system
• Developing and running performance evaluation tests under various conditions
• Creating regular weekly reports
• Participating in weekly meetings (in-person or remote)

Student qualifications required:

• Proficiency in Python programming
• Excellent understanding of Networking protocols. Must have completed INFR1411U/INFR1421U or equivalent.
• Good understanding of and hands-on experience with network security toolboxes is an asset.
• Hands-on experience with Machine Learning theory and libraries is an asset.

Expected training/skills to be received by the Student:

• Cybersececurity tools
• Machine Learning Programming
• Python Scripting

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR Award

 

Supervisor name: Stephen Marsh

Project title: Zero Trust Security

Summary of research project: Traditional access models use a combination of something you know, something you are and something you have; however, these are prone to forgetfulness, hacking and loss. Combinations work well but security does not have to be so hard on humans. The concept of Zero Trust (and associated concepts such as Zero Interaction Authentication) have provided a strong enterprise security model that does not rely on the concept of ‘trusted zones’ or ‘continuous authorization’ In this project we will be examining Zero Trust architectures with a view to the creation of working applications alongside an industry partner.

Student responsibilities/tasks:

• Assist with literature review
• Assist with architectural design
• Coding
• Testing
• Liaison with industry partner
• Assist with documentation/publication of results.

Student qualifications required:

• Minimum 3rd year B.IT.

Expected training/skills to be received by the Student:

• Creation of literature reviews and documents
• Coding for Zero Trust applications
• An understanding of advanced IT Security
• Project management

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR Award