Muhammad Bilal Qureshi

Back to list

Muhammad Bilal Qureshi

Graduate of 2026
BS Electrical Engineering
mq08079@st.habib.edu.pk
Linkedin Profile

Aspiration Statement

I am deeply interested in automation and artificial intelligence. I leverage robotics through my capstone on autonomous vehicles, while possessing knowledge in deep learning and LLMs enhanced by various projects.

Core Skills

  • C/C++
  • Deep Learning
  • Matlab and Simulink
  • Python
  • Robotic Operating System (ROS2)

Core Competencies

  • Adaptability
  • Planning
  • Strategic Thinking
  • Takes Initiative

Preferred Career Paths

First priority: Robotics Engineer

Second priority: AI Engineer - Deep Learning

Third priority: Systems Engineering

Academic Awards / Achievements

  • Dean's List 2023, 2025

Experience

Leadership / Meta-curricular

  • President, IEEE (Institute of Electrical and Electronics Engineers)
  • Academic Affairs Representative, Habib University Student Government

Internship / Volunteer Work

  • Tutor, Epsilon Academy (September 2022 – May 2026)
  • Teaching Assistant - Systems Engineering and Project Management, Habib University (September – December 2025)
  • Research Intern, Indus Motor Company Ltd (June – August 2025)
  • Teaching Assistant - Computer Architecture, Habib University (September – December 2024)

Final Year Project

Project Title

Autonomous Tow Truck - Enhanced Safety and Obstacle Detection

Description

Autonomous tow truck for IMC - Toyota. We started with systems engineering to analyze use cases and determine the optimal design for the vehicle. Our software implementation leverages Autoware, which is built on the ROS2 framework with visualization on RViz, and we use Docker to maintain consistent development environments. Throughout the project, continuous use of Git ensures proper teamwork and a reliable version history. A critical component of my part is the integration of Hardware-in-the-Loop (HIL). On the sensor front, I integrated a Livox MID360 3D LiDAR with Autoware. This setup enabled the successful implementation of the Normal Distributions Transform (NDT) algorithm to achieve accurate spatial localization for the autonomous system.

Project Pictures