Teaching

Teaching Philosophy

I believe in creating an inclusive and engaging learning environment where students can develop both theoretical understanding and practical skills. My teaching approach emphasizes hands-on experience, real-world applications, and fostering critical thinking.

Teaching Experience

• ECE1724HF – Bio-inspired Algorithms for Smart Mobility
  Principles and applications of bio-inspired algorithms in transportation and mobility systems.
• CSC165H1 – Mathematical Expression and Reasoning
  Logical reasoning, proof techniques, and mathematical expression skills for computer science.
• ECE324 – Introduction to Machine Intelligence
  Fundamentals of machine learning, neural networks, and AI applications.
• MIE443 – Mechatronics Systems: Design Integration
  Design and integration of mechanical, electrical, and computer systems in mechatronics.
• APS106 – Fundamentals of Computer Programming
  Introduction to programming concepts using Python for engineering applications.
• APS360 – Artificial Intelligence Fundamentals
  Core AI concepts including supervised/unsupervised learning and neural networks.
• APS1070 – Foundations of Data Analytics and Machine Learning
  Data preprocessing, statistical learning, and basic machine learning models.
• APS1080 – Introduction to Reinforcement Learning
  RL concepts, algorithms (Q-learning, policy gradients), and applications.
• CIV100 – Mechanics
  Statics and dynamics fundamentals, force analysis, and structural mechanics.
• CIV332 – Transportation II: Performance
  Traffic flow theory, capacity analysis, and performance modeling.
• ECE311H1 – Dynamic Systems and Control
  Differential equation models and control of mechanical, electrical, and electromechanical systems.