mentorship

Mentorship opportunities and guidance for students interested in computer vision, machine learning, and AI research.

Mentorship Philosophy

I believe in fostering a collaborative learning environment where students can develop both technical skills and research independence. My mentorship approach focuses on:

  • Hands-on Research Experience: Direct involvement in cutting-edge computer vision and AI projects
  • Academic Growth: Supporting students in developing research skills, writing papers, and presenting at conferences
  • Career Development: Guidance on academic and industry career paths in AI and computer vision
  • Project Management: Teaching modern research methodologies including Kanban and agile approaches

Current Mentorship Opportunities

Graduate Research Assistants (2024-present)

I am currently co-mentoring 2 PhD and 6 master students in the sports analytics research group at the University of Waterloo. Our collaborative work focuses on:

  • Ice Hockey Analytics: Broadcast video analysis, player tracking, and game event detection
  • Computer Vision Systems: Homography estimation, object detection, and action recognition
  • Machine Learning Applications: Continual learning, neural networks, and deep learning models

Undergraduate Research Opportunities

I regularly supervise undergraduate students in research projects. Current areas include:

  • Computer Vision Projects: Image processing, object detection, and video analysis
  • Machine Learning Applications: Neural networks, deep learning, and AI model development
  • Sports Analytics: Data analysis, visualization, and statistical modeling
  • Medical AI: Ultrasound data analysis and medical image processing

Mentorship Approach

Research Methodology

  • Modern Project Management: Utilizing tools like Jira and Kanban for academic research coordination
  • Iterative Development: Encouraging rapid prototyping and continuous improvement
  • Collaborative Learning: Regular group meetings, code reviews, and knowledge sharing sessions

Skill Development Focus

  • Technical Skills: Python, PyTorch, OpenCV, computer vision algorithms
  • Research Skills: Literature review, experimental design, statistical analysis
  • Communication Skills: Academic writing, presentation, and collaboration
  • Professional Development: Conference submissions, networking, and career planning

Past Mentorship Success Stories

Graduate Students

  • Sepehr Ghavam (2024): Video object segmentation for long videos
  • Simon Frew (2023): Neural networks on dietary data with measurement error handling

Undergraduate Students

  • Stephie Liu & Jonathan Dumanski (2025): Rink agnostic homography estimation for Ice Hockey
  • Soyeon Jang (2024): Doppler Ultrasound image classification
  • Michael Frew (2023): Doppler Ultrasound data analysis
  • Xin Xue (2023): Continual learning on custom diffusion models
  • Zeyad Moustafa (2023): Long video object segmentation dataset design
  • David Eric Austin (2022): Neural networks on dietary data
  • Anita Hu (2020): Continual learning for classification tasks

Getting Involved

For Current Students

If you’re interested in research opportunities, please reach out with:

  • Your academic background and interests
  • Specific areas of computer vision or AI you’d like to explore
  • Your availability and commitment level
  • Any relevant coursework or projects

For Prospective Students

I welcome inquiries from students interested in:

  • Graduate Studies: PhD and Master’s opportunities in Systems Design Engineering
  • Research Collaborations: Joint projects and publications
  • Industry Partnerships: Applied research in sports analytics and medical AI

Contact Information

For mentorship inquiries, please contact me through:

  • Email: [Your email address]
  • Office Hours: [Your office hours]
  • Research Group: Sports Analytics Lab, University of Waterloo

“The best way to learn is to teach, and the best way to grow is to mentor others.”