I am Engr. Faria Sheikh, lecturer at the Computer Engineering Department . With over 10 years of experience in academia, my primary research interests lie in Control Engineering, Digital Systems and Electronics. My teaching philosophy centers around providing students with a strong theoretical foundation, while also enabling them to use critical and analytical thinking.
I have taught a variety of undergraduate -level courses in the Computer and Electrical Engineering Department. Below are the courses I have been involved in teaching:
Undergraduate Level
- Electric Circuit Analysis
- Digital Logic Design
- Control Engineering
- Artificial Intelligence and Machine Learning
- Computer Programming
- Electromagnetic Theory
- Microwave Engineering
I am also actively involved as a Member of the Curriculum Design and Project Coordinator for the Steam Xplorer Camp at National University of Technology (NUTECH), Islamabad. The camp focuses on Game Development, Web Development, AI & Robotics, and Programming Drones, offering hands-on learning experiences to participants. In this role, I contributed to the development of innovative curricula, coordinated project teams, and ensured the successful execution of various educational initiatives. My work bridges academic teaching and practical, technology-driven projects, enriching the educational journey for students at different learning levels.
My research lies at the intersection of Control Systems, Power Systems, and Machine Learning. I am particularly focused on Economic Load Dispatch (ELD) in microgrids, where I explore the application of meta-heuristic approaches to optimize energy distribution. My work seeks to improve the efficiency, sustainability, and cost-effectiveness of microgrid operations by leveraging advanced computational techniques and intelligent algorithms.
By integrating machine learning with traditional control and power systems, I aim to develop innovative solutions that enhance the management of distributed energy resources, facilitating smarter and more resilient energy grids.
UrbanScope Mapping and Navigation (2022–2023): This project addresses the challenges of navigation in GPS-deprived environments, such as military and surveillance systems, by developing a Visual Odometry-based system. Using sequential images and sensors, the system determines an object's position and orientation, reducing reliance on GPS/GNSS. The project aims to create a deep learning-driven approach for real-time navigation and urban mapping, with applications in autonomous systems for disaster zones and unmanned military vehicles. By minimizing dependence on GPS, the system enhances efficiency and resilience in GPS jamming situations, supporting defense and commercial surveillance needs.
