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.”