I am an accomplished engineer with a Ph.D. specializing in the design and optimization of renewable energy systems. My expertise lies in applying data-driven and optimization methodologies to enhance the operational control of microgrids. My work focuses on integrating renewable energy sources, balancing the trade-offs between the electric grid and microgrid, and utilizing advanced energy storage systems. I am dedicated to fostering the development of resilient and sustainable energy systems, and my research aims to push the boundaries of what's possible in energy management. In addition to my work in energy systems, I possess significant experience in power system optimization and techno-economic feasibility studies. My proficiency extends to developing machine learning, deep learning, and reinforcement learning models using Python for various applications. I am particularly skilled in applying AI-based techniques for forecasting renewable resources and electrical load demands. This combination of skills and experience enables me to contribute to innovative solutions in the field of energy. Throughout my career, I have developed and delivered educational content on Machine Learning with Python, Time Series Analysis Using Deep Learning, and Optimization in Python and GAMS. My academic background includes a Doctorate in Civil Engineering from Concordia University, where I led research in renewable resource forecasting and energy system modeling using stochastic programming. My goal is to share my knowledge and expertise to help others understand and advance the frontiers of energy systems and optimization.