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Faculty of Business and Information Technology Project Summaries

Supervisors  

Alvaro Quevedo | Amirali Abari |Andrew Hogue | Andrew Hogue | Khalil El-Khatib | Pooria Madani | Stephen Jackson

 

Supervisor name: Alvaro Quevedo

Project title: Prototyping customizable immersive onboarding experiences for skill development

Summary of research project: Immersive technologies are becoming widely adopted in education, health care and training, among many other fields. The current availability of both off-the-shelf hardware and software development tools has facilitated the development and availability of numerous applications. However, the technology itself is a one-size-fits-all and overlooking accessibility and customization can negatively impact the user experience. This research focuses on prototyping immersive onboarding experiences aimed at further understanding the role of VR expertise towards proposing novel forms of interactions to lower cognitive load and increase proficiency.

Student responsibilities/tasks:

  • Conduct a literature review.
  • Prototype immersive onboarding user experiences for virtual, augmented, and mixed reality using consumer-level VR headsets provided during the research.
  • Integrate consumer-level, custom-made user interfaces and 3D user interfaces relevant to the onboarding experience to determine the effects on cognitive load.
  • Conduct preliminary user studies to compare consumer-level experiences and those developed.

Student qualifications required:

  • Experience with virtual reality development.
  • Experience with 3D printing.
  • Courses on computer graphics, industrial design for game hardware (or equivalent with computer assisted design and manufacturing), human-computer interaction (equivalent).
  • Familiarity with 3D authoring tools for content creation.
  • Experience with open electronics.

Expected training/skills to be received by the Student:

  • Fundamentals of scientific research and the research method.
  • Scientific writing.
  • Collaborative research.
  • Immersive technology development and testing.
  • Designing and conducting research user studies.

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Amirali 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 their involvement in this Project.
  • This Project provides a unique opportunity to the candidate for fostering their 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: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Andrew Hogue

Project title: Exploring Dynamic Realistic Avatars in Games

Summary of research project: In this project we will explore the use of photorealistic dynamic avatars in video games. To do so, the successful student will work with our research team to capture a series of high quality avatar performances (full-body humans and close up faces) and work with us to develop a processing pipeline to Unity/Unreal. We will be using state of the art Machine Learning algorithms and techniques for capturing avatars (i.e. gaussian splatting based avatars) and more standard volumetric video capture techniques. The objectives for the student will be to evaluate the use of these dynamic avatars for game applications, and develop workflows to re-style the 4D avatars with AI techniques. 

Student responsibilities/tasks:

  • Literature Review on Realistic Gaussian Avatars and stylization.
  • Planning performances to capture.
  • Recording performances with capture studio in the lab.
  • Developing scripts and methods to process the data into a Gaussian Avatar (using exiting ML projects).
  • Evaluating this method for use in video games, understanding and defining the metrics involved.
  • Create a Research Poster.
  • Write first draft of a conference research paper.

Student qualifications required:

  • Must be excited to work in this space and be motivated to work independently and with a team.
  • Must be able to problem solve and think critically and be able to find and read research papers effectively.
  • Game Engine Development skills (Unity/Unreal) are necessary.
  • Ability to navigate python scripts and basic python scripting is necessary.

Expected training/skills to be received by the Student:

  • Development Skills (Unity/Unreal).
  • Critical thinking (paper reading/literature review).
  • Paper writing (conference paper draft).
  • Presenting research (research poster presentation).
  • AI (exploring state-of-the-art AI and ML techniques and expanding on them).

Length of award: 14 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Andrew Hogue

Project title: Interactive 4D Avatars for Games

Summary of research project: In this project, students will explore the space of interactive 4D realistic avatars. They will explore emerging techniques and technologies to record and capture photorealistic 3D recordings of performances in our volumetric capture studio and evaluate the use of these avatars for Games and XR applications.

Student responsibilities/tasks:

  • Students will be tasked with learning about Volumetric Video, Gaussian Splatting, and Gaussian Avatars and to work with other students on using existing tools and machine learning algorithms to train a set of Gaussian avatars for use in a research study on the use of photorealistic avatars in interactive scenarios.

Student qualifications required:

  • Students must be motivated to learn new skills related to artificial intelligence and machine learning.
  • Open to Computer science and/or IT/Game Dev/Engineering students.
  • Introductory knowledge of Machine Learning or motivation to learn ML techniques are desired.
  • Basic Python skills are necessary.
  • Unity/Unreal Engine knowledge is preferred.

Expected training/skills to be received by the Student:

  • Knowledge and expeirence with Volumetric Video tools.
  • Knowledge and experience creating Gaussian Splats.
  • Practicing python and machine learning skills.
  • Practicing Problem Solving and Critical Thinking skills.
  • Experience with Unity/Unreal Engine.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Khalil El-Khatib

Project title: Enhancing CubeSat Cybersecurity: A TinyML Approach to Anomaly-Based Intrusion Detection

Summary of research project: CubeSats are a type of nanosatellite that weigh less than 1.33 kg per unit and have dimensions of 10 x 10x10cm . These small, cost-effective satellites have made space research and education more accessible by providing affordable access to space. CubeSats play an important role in various missions, including environmental monitoring, disaster prevention, communications, scientific research, and military applications. The growing number of CubeSats raises significant cybersecurity concerns. The ground segment, link segment, and space segment are all subject to cyberattacks, which can range from data tampering to kill radio commands that disable the CubeSat.

Student responsibilities/tasks:

  • The student will be responsible of running a satellite simulation to collect various data point from the simulation.
  • Using the data set, student should be able to build a small Machine learning model that can detect anomalies.
  • The student should also prepare weekly reports about their work.
  • The student should also participate in weekly meetings.

Student qualifications required:

  • The student should be enrolled in the IT networking and security program, must have taken the machine learning course, and must have taken the Network security course.
  • Student should have finished third year of the BIT program.

Expected training/skills to be received by the Student:

  • Configure and run a space satellite communication.
  • Build machine learning models for anomaly detection.
  • Collect, analyze data and communicate results in oral and written form.

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Pooria Madani

Project title: Federated Learning Machine Learning

Summary of research project: This project aims to develop a simulation environment tailored for testing and evaluating federated learning algorithms in satellite constellations. Federated learning is a distributed machine learning approach where models are trained across multiple devices or nodes, such as satellites, without centralized data aggregation. This is particularly relevant for satellite constellations, where communication bandwidth is limited, and data privacy is crucial.

Student responsibilities/tasks:

  • Be proficient in Python to develop full orbital simulations of satellite constellations.
  • Implement and visualize various orbital configurations, ensuring accurate simulation of satellite motion.
  • Research existing work in satellite constellations and related algorithms.
  • Document all work, including code and findings, on the research lab's wiki for future reference.

Student qualifications required:

  • Strong understanding of Python programming.
  • Comfortable with geometry and mathematics, especially implementing mathematical formulas in Python.
  • Experience with scientific Python libraries (e.g., NumPy, SciPy, Matplotlib).
  • Familiarity with machine learning is a bonus.

Expected training/skills to be received by the Student:

  • Gain sufficient knowledge of orbital mechanics to effectively build the simulation environment.
  • Acquire a solid understanding of backpropagation-based learning algorithms to effectively test them in a federated learning environment.

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Stephen Jackson

Project title: Language Analysis of AI-Generated Content

Summary of research project: The key aim of this project is to examine the tone of AI-generated content. Using a computer-assisted content analysis program (which will be provided), students will analyze language use and verbal styles of AI-generated content against English-language texts. The goal will be to explore linguistic differences in word usage, and to undertake further analysis as to why differences might exist.

Student responsibilities/tasks:

  • Students will be required to learn and undertake tutorials in the use of a computer-aided text analysis program (Windows based).
  • Be able to utilize a computer-aided text analysis program to perform content analysis.
  • Be able to collect, analyze and extract data and distil research findings.

Student qualifications required:

  • Be able to undertake comparative analysis i.e., identify similarities and differences between items.
  • Be able to clearly document your findings and express your ideas.
  • Familiarity with linguistic analysis and/or statistical analysis is a bonus.

Expected training/skills to be received by the Student:

  • Gain sound knowledge of content (linguistic) analysis software.
  • Establish a conceptual understanding of linguistic terminology.
  • Learn and establish essential critical thinking and problem-solving skills.

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: Ontario Tech STAR