Teaching
Courses & Educational Activities
Current Courses
Signals and Systems
Spring 2024
This course introduces fundamental concepts in signals and systems, providing students with the mathematical tools and techniques necessary for analyzing and processing signals. Topics include:
- Continuous-time and discrete-time signals
- Linear time-invariant systems
- Fourier series and Fourier transform
- Laplace transform
- Z-transform
- Filter design and applications
Advanced Topics in Machine Learning for Energy Systems
PhD Program
This advanced course explores the application of machine learning techniques to energy systems, with a focus on renewable energy integration and optimization. Students learn to:
- Apply reinforcement learning to energy optimization problems
- Develop predictive models for renewable energy generation
- Design control strategies for battery storage systems
- Analyze energy market data and optimize trading strategies
Teaching Philosophy
My teaching approach emphasizes:
Connecting Theory to Practice: Bridging theoretical concepts with real-world applications to enhance student understanding and motivation.
Interactive Learning: Encouraging active participation through discussions, problem-solving sessions, and practical assignments.
Research Integration: Incorporating cutting-edge research findings into course content to expose students to the latest developments in the field.
Interdisciplinary Perspectives: Highlighting connections between electrical engineering, computer science, economics, and environmental studies to foster holistic understanding of energy systems.
Student Supervision
I supervise graduate students working on topics related to:
- Battery storage optimization
- Renewable energy integration
- Machine learning applications in energy systems
- Smart grid technologies
If you are interested in working with me as a PhD student or for a research project, please contact me to discuss potential research directions.