Syed Mujtaba Rizvi

Syed Mujtaba Rizvi

Syed Mujtaba Rizvi

Class of 2020
BS Electrical Engineering

Aspiration Statement

I have a passionate interest in working in the fields of telecommunication, IoT, AI, and electrical power engineering. I look forward to working with a dynamic organization and engaging in projects that challenge both my academic and interpersonal skills.

Core Skills

  • Adaptability
  • Arduino
  • C++
  • MATLAB
  • Problem solving
  • Python
  • Teamwork
  • Verilog HDL

Academic Awards / Achievements

  • HU TOPS 100% Scholarshi

Experience

Leadership / Meta-curricular

  • Former Chairman, Meezan Hostel Karachi (Meezan Hostel System is a welfare organization helping 80 students each year to succeed through their Intermediate studies, by providing good accommodation and academic assistance)

Internship / Volunteer Work

  • Apprenticeship, Mindstorm Studios (July 2022 – September 2022)
  • Internship, Bank Alfalah (July 2022 – August 2022)
  • Internship, Pakistan Water and Power Development
  • Authority (WAPDA) (June 2017 – August 2017)

Publications / Creative Projects

  • Presented hardware project on efficient fuel injection mechanism in Habib University's Open House

Final Year Project

Project Title

Predictive Modelling and IoT-based Remote Monitoring of a Gasoline Generator

Description

In my final year project, an approach is proposed to improve the work life of workers who continuously monitor electric generators to ensure their efficient operation and uninterrupted electricity supply. The proposed solution is an IoT-based remote monitoring system that collects data on the generator's health and saves it on a cloud platform. A thermal equivalent circuit model is identified to establish a relationship between the temperature data collected from the generator's casing and the temperatures at crucial internal parts such as the rotor, stator, and end cap. It is observed that the model accurately represents the thermal dynamics of the generator, and it is concluded that with proper resources, this model can be applied to any type of generator to predict the life of its internal crucial parts.