MOHAMMAD DILAWAR ALI SAEED

Back to list

MOHAMMAD DILAWAR ALI SAEED

Class of 2024
BS Computer Engineering
Linkedin Profile

Aspiration Statement

As per my strengths, interests, and experiences, I would like to work in Data Science/Analysis, Project/Product Management, or Development.

Core Skills

  • C++
  • Data Science/Anaylsis
  • Empathy
  • Leadership
  • Python
  • PowerBI
  • Project Management
  • Teamwork

Academic Awards / Achievements

  • Habib Excellence Scholarship
  • Launched 'Rapidbazar' - Habib University’s Student Vendor Promotion Event
  • 3+ Years in the Habib University Student Government (Various Roles)
  • Stanford University Summer International Honors Program 2023

Experience

Leadership / Meta-curricular

  • CE Batch Representative, Habib University Student Government (February 2023-February 2024)
  • Chair Food & Hygiene Cabinet, Habib University Student Government (February 2022-February 2023)

Internship / Volunteer Work

  • TA Computer Architecture, Habib University (January-May 2023)
  • Summer Intern, Bank al Habib Ltd (June-July 2022)
  • SEO Content Writer, Ink & Ivory (August -October 2021)

Publications / Creative Projects

  • ArcGIS & Image Processing to Detect Urban Development in Karachi (FYP):
  • Habib University Cafeteria Management System using Django and PyQt6:
  • Habib University Parking Lot Occupancy Detection using Classical Image Processing:
  • Hangman in Python:
  • KIA Motors Pakistan Comprehensive Analysis (PESTEL and Porter 5 Forces):

Final Year Project

Project Title

SustainiVision

Description

This capstone project addresses the critical challenges of urban deforestation and enhancing green spaces in Karachi through the use of geospatial technologies and remote sensing. By employing USGS Landsat 8-9 satellite imagery and NDVI image processing, this study leverages the robust capabilities of ArcGIS and Python programming within a Jupyter Notebook to monitor environmental changes systematically. The project meticulously tracks bi-weekly vegetation changes, offering detailed insights into the dynamics of urban greenery. The results reveal significant fluctuations in green coverage, equipping city planners with the data needed to devise effective sustainable development strategies.