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Usama N. AI, Machine Learning and Computer Vision Platforms

My name is Usama N. and I have over 7 years of experience in the tech industry. I specialize in the following technologies: Computer Vision, Machine Learning, Python, TensorFlow, Keras, etc.. I hold a degree in Master's degree, Bachelor of Applied Science (BASc). Some of the notable projects I’ve worked on include: Flask Application for Satellite Imagery, Satellite Imagery of Bike Lanes with Instance Segmentation Annotations. I am based in Gujranwala, Pakistan. I've successfully completed 2 projects while developing at Softaims.

I employ a methodical and structured approach to solution development, prioritizing deep domain understanding before execution. I excel at systems analysis, creating precise technical specifications, and ensuring that the final solution perfectly maps to the complex business logic it is meant to serve.

My tenure at Softaims has reinforced the importance of careful planning and risk mitigation. I am skilled at breaking down massive, ambiguous problems into manageable, iterative development tasks, ensuring consistent progress and predictable delivery schedules.

I strive for clarity and simplicity in both my technical outputs and my communication. I believe that the most powerful solutions are often the simplest ones, and I am committed to finding those elegant answers for our clients.

Main technologies

  • AI, Machine Learning and Computer Vision Platforms

    7 years

  • Computer Vision

    4 Years

  • Machine Learning

    1 Year

  • Python

    6 Years

Additional skills

  • Computer Vision
  • Machine Learning
  • Python
  • TensorFlow
  • Keras
  • Data Annotation
  • Video Annotation
  • Audio Transcription
  • PyTorch

Direct hire

Potentially possible

Previous Company

Techlogix

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Experience Highlights

Flask Application for Satellite Imagery

I developed a web application using YOLO (You Only Look Once) to detect bike lanes and roads in satellite imagery. Users can upload satellite images and receive real-time annotations for bike lanes and roads. The app features a user-friendly interface, high accuracy, and quick processing, leveraging our annotated dataset. It serves urban planners, researchers, and developers for visualizing and analyzing bike lane infrastructure. Available for academic and research purposes upon request.

Satellite Imagery of Bike Lanes with Instance Segmentation Annotations

This dataset includes satellite images annotated with instance segmentation for roads and bike lanes. Covering diverse urban areas, the high-precision annotations were made by a dedicated team. Applications include urban planning, transportation analysis, autonomous navigation, and machine learning research. The data is in standard image formats with annotations in JSON or COCO format, ensuring high resolution and geographical diversity. Available for academic and research use upon request.

Education

  • National University of Sciences and Technology

    Master's degree in Data Science

    2020-01-01-2022-01-01

  • National University of Sciences and Technology

    Bachelor of Applied Science (BASc) in Mathematics

    2016-01-01-2020-01-01

Languages

  • English
  • Urdu

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