Mr. Muhammad Bilal Rehman

Lecturer (CS)

Introduction

I am Muhammad Bilal Rehman, serving as a Lecturer in the Department of Computer Science at the National University of Technology, Islamabad. My core research interests include Artificial Intelligence (AI), Machine Learning, and Data Science, with a focus on leveraging data-driven approaches to address real-world challenges, particularly in NLP. As an educator, I am committed to fostering a deep theoretical understanding in my students while equipping them with practical skills to thrive in the ever-changing technology landscape.

Experience

Lecturer, Department of Computer Science, National University of Technology (2024 –Present)

  •  
  • Teach undergraduate courses in Data Structures and Programming Fundamentals.

Qualification

  • Academia:
    • M.S in Data Science, Air University, Islamabad (2023)
    • B.S in Computer Systems Engineering, UET Peshawar (2019)
  • Certifications:
    • NLP Specialization, Deeplearning.ai Coursera (2022)
  • Trainings
    • AI and Deep Learning Training, IBM Coursera (2022)
    • Advanced Machine Learning with TensorFlow, IBM Coursera(2022)

Taught Courses

I have taught a variety of undergraduate and graduate-level courses in the Computer Science Department. Below are the courses I have been involved in teaching:

  • Programming Fundamentals (CS120)
    An introductory course focusing on programming concepts using Python, covering topics such as variables, control structures, functions, and basic data structures.
  • Data Structures and Algorithms (CS210)
    A core course that covers fundamental data structures (e.g., arrays, linked lists, trees, graphs) and algorithmic techniques (e.g., sorting, searching, recursion) with a focus on computational complexity.

Projects

  • Extracted cognitive relationships between citing and cited papers in academic research (2022-2023).
  • Prognostic maintenance of hydro-power sub systems using machine learning algorithms
  • Extracted drugs and medicine names from academic research articles using NLP and pre-trained models.