teaching
Teaching experience and courses in computer vision, machine learning, and signal processing at University of Waterloo and Shiraz University.
University of Waterloo
Graduate Courses
SYDE 672: Statistical Image Processing and Multidimensional Modeling (2022)
- Graduate level course for PhD and M.Sc students
- Course perception surveys scores: 4.1/5
- Topics: Inverse problems, multidimensional modeling, large-scale statistical problems, generative models
Teaching Assistant Experience (2020-2022)
- BME 252: Linear Signals and System (Spring 2022)
- MTE140: Algorithms and Data Structures (Winter 2022)
- SYDE 252: Linear Signals and System (Fall 2021)
- BME 252: Linear Signals and System (Spring 2021)
- SYDE 532: Intro to Complex Systems (Winter 2021)
- SYDE 121: Digital Computation (Fall 2020)
- SYDE 675: Pattern Recognition (Winter 2020)
Teaching Certificates
Fundamentals of University Teaching (FUT) Certificate (2021-2022)
- University of Waterloo Center for Teaching Excellence (CTE)
- Three microteaching sessions
- Six workshops covering:
- Teaching methods
- Effective lesson planning
- Supporting student mental health
- Shaping classroom dynamics
- Social anxiety in classroom
- Statements of teaching philosophy
Shiraz University
Teaching Assistant Experience (2012-2016)
- Advanced Pattern Recognition (Spring 2016)
- Computer Vision (2014-2015)
- Statistical Pattern Recognition (Fall 2012)
Mentorship
Graduate Research Assistants (2024-present)
- Co-mentoring 2 PhD and 6 master students in the sport analytics group
- Utilizing Kanban methods such as Jira for academic project management
Graduate Research Assistants (2023-2024)
- Sepehr Ghavam (Winter 2024): Video object segmentation for long videos
- Simon Frew (Winter 2023): Neural networks on dietary data with measurement error
Undergraduate Research Assistants (2020-2024)
- Stephie Liu (Spring 2025): Rink agnostic homography estimation for Ice Hockey
- Jonathan Dumanski (Winter 2025): Rink agnostic homography estimation for Ice Hockey
- Soyeon Jang (Winter 2024): Doppler Ultrasound image classification
- Michael Frew (Fall 2023): Doppler Ultrasound data analysis
- Xin Xue (Fall 2023): Continual learning on custom diffusion model
- Zeyad Moustafa (Spring 2023): Designing a long video object segmentation dataset for continual learning
- David Eric Austin (Winter 2022): Neural networks on dietary data
- Anita Hu (Spring 2020): Continual learning for classification tasks
Undergraduate Capstone Project (2025-present)
- Supervising a team of 6 undergraduate students
- Project: An LLM-based coach assistant questioning and answering system via the RAG architectural approach