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Results-driven Computer Engineering student at NUST, specializing in artificial intelligence, machine learning, and image processing. Expert in developing complex algorithms for data analysis and predictive modeling.

My expertise spans biomedical AI research, computer vision, and data science. With hands-on experience in industry-standard tools and frameworks, I transform technical concepts into innovative solutions that drive real-world impact.

Muzammil Nawaz Khan

Featured Projects

Explore my portfolio of AI and machine learning projects, showcasing cutting-edge solutions in computer vision, deep learning, and data science.

Luggage Threat Detection

Luggage Threat Detection

Developed ANN architecture for image classification of potential threats in luggage images with high accuracy in threat identification.

PythonANNOpenCVImage Classification
License Plate Recognition

License Plate Recognition

Created pipeline for license plate localization using edge detection and implemented robust plate isolation system.

PythonOpenCVNumPyComputer Vision
Braille Digits Recognition

Braille Digits Recognition

Built system to recognize Braille characters through dot pattern analysis and distance metrics for character differentiation.

PythonOpenCVPattern Recognition
Cat Dog Classification

Cat Dog Classification

Implemented CNN models with and without pooling and dropout layers, demonstrating regularization techniques.

PythonTensorFlowKerasCNN
Skin Image Segmentation

Skin Image Segmentation

Designed segmentation system using Connected Component Labeling and achieved accurate results with IoU metrics.

PythonOpenCVImage Segmentation
Retinal Image Segmentation

Retinal Image Segmentation

Developed method for segmenting retinal structures using point and multi-level thresholding techniques.

PythonOpenCVMedical Imaging

Professional Experience

Biomedical AI Research and Development Intern

RiseTech - Islamabad, Pakistan

July 2024 - Sept 2024

  • Developed 3D medical image segmentation models using BraTS2020 dataset for brain tumor diagnosis
  • Implemented SegFormer3D and UNet 3D architectures using TensorFlow and PyTorch
  • Optimized deep learning models for improved accuracy in tumor segmentation
  • Evaluated model performance using dice scores and IoU metrics
  • Streamlined workflows by establishing models as benchmarks for future comparisons
PythonTensorFlowPyTorchMedical Imaging3D SegmentationDeep Learning

Skills & Technologies

AI & Machine Learning

TensorFlowPyTorchDeep LearningComputer VisionCNNRNNTransfer Learning

Programming & Tools

PythonOpenCVNumPyMATLABC++Java

Domain Expertise

Medical ImagingImage SegmentationPattern Recognition3D SegmentationEdge DetectionMorphological Operations

Frameworks & Libraries

Scikit-learnLangChainStreamlitGoogle ColabMongoDBMySQL

Get in Touch

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