Yabudullah Ahmed Bakhtiar

Yabudullah Ahmed Bakhtiar

Class of 2023
BS Electrical Engineering
Minor: Computer Science
Minor: Electrical and Computer Engineering

Aspiration Statement

I aim to engineer solutions using technology, innovation, creativity, and sustainability to address real-world challenges and create a positive impact on society. I have a strong desire to constantly learn, grow, and collaborate with experts in my field.

Core Skills

  • C++
  • CAD
  • Collaboration and Teamwork
  • Creativity and Innovation
  • Critical Thinking
  • Problem-Solving
  • Python
  • Research
  • SolidWorks

Academic Awards / Achievements

  • UGRAD Fall 2023 Community Development Initiative Grant
  • Dean's List - Spring 2022, Wright State University
  • UGRAD Scholar - Spring 2022, Wright State University

Experience

Leadership / Meta-curricular

  • Semi-finalist, Unilever Hackathon 2022
  • Khidmat Project Supervisor, Parvaaz

Internship / Volunteer Work

  • Lead R&D Engineer, Paradigm EVs (May 2022 – January 2023)
  • Cultural Ambassador, U.S. Department of State (January 2022 – May 2022)
  • Peer Tutor, Habib University (September 2021 – December 2021)
  • Undergraduate Researcher, Habib University (July 2021 – September 2021)
  • Internee, Integrated Dynamics (June 2021 – July 2021)

Publications / Creative Projects

  • Parvaaz Volunteer Management platform for UGRAD CDI

Final Year Project

Project Title

Mapping International Roughness Index of Road & Environmental Parameters on Road Networks in Karachi Using IoT

Description

The project aims to develop an inexpensive inertial profilometer. Equipped with IoT technology, the device will be used to map the road quality in Karachi, Pakistan, by collecting data from various nodes across the city. The collected data will be analyzed to identify areas requiring repair or maintenance in order to enhance road conditions. Additionally, the project may also gather environmental data, including temperature, humidity, and air quality, to explore their impact on the roads and the surrounding environment. Furthermore, the project intends to integrate a low-cost camera to train a computer vision model for future identification of road roughness using visual data. This innovation would enable non-contact measurement of road quality, offering a cost-effective and efficient solution for road maintenance and improvement.