PROFILE SUMMARY

Mr. Shahid Raza is an emerging computer scientist and educator currently pursuing his Ph.D. in Computer Science at the Islamic International University Islamabad. He completed his MS in Computer Science from Shaheed Zulfiqar Ali Bhutto University and BS in Computer Science from the Federal Urdu University of Arts, Science & Technology. With academic training spanning programming, system design, digital logic, and applied computing, he integrates theoretical and practical learning effectively.

He has served as Lab Instructor at Shifa Tameer-e-Millat University and Lab Demonstrator at Shaheed Zulfiqar Ali Bhutto University, conducting labs in Assembly Language, ICT, C++, OOP, Data Structures, Digital Logic Design, Database Systems, and Computer Networks. His experience includes developing lab manuals, supervising projects, and strengthening students’ problem-solving skills.

Before academia, Mr. Shahid worked as a Software Development Intern at CDigital on the AJKED Record Digitization Project, and as a Computer Operator at INSPIRE GOC, where he managed IT operations, data systems, and troubleshooting. He also served in customer support at Sybrid Pvt. Ltd. (Telenor Pakistan Project) and worked as a bilingual Content Writer and Social Media Admin at AQ TV.

His research includes deep learning–based early detection of skin lesions and IoT-based water tanker automation systems. Skilled in PHP, SQL, HTML/CSS, Laravel, Vue.js, MySQL, Git/GitHub, and microcontroller programming, he continues to build interdisciplinary expertise across software development, teaching, and applied research.

RESEARCH AREAS / INTERESTS

1. Deep learning and computer vision for medical image analysis (skin lesion detection).
2. Machine learning algorithms for classification and pattern recognition.
3. IoT-based automation systems integrating sensors, microcontrollers, and real-time data processing.
4. Software development for web-based systems using PHP, SQL, Laravel, and front-end technologies.
5. Data management, digitization, and workflow automation for organizational systems.
6. Hardware–software integration for embedded and smart-device applications.
7. Comparative analysis of neural network architectures and their practical applications.