Abdullah Hatif

Analytical and motivated Mathematics major with MATLAB skills and hands-on experience in machine learning, numerical modeling, and image processing. Experienced in both independent research and collaborative academic projects. Seeking opportunities to apply mathematical and coding skills in real-world problem-solving.

Image Compression using DCT
Advanced Linear Algebra — MATLAB

• Built a full JPEG-style compression pipeline using Discrete Cosine Transform (DCT)
• Compressed grayscale and RGB images using both linear and JPEG quantization techniques
• Reconstructed images from quantized data and compared results across multiple compression levels

Support Vector Machine and SVD Dimensionality Reduction
Machine Learning — MATLAB

• Implemented a soft-margin SVM classifier for MNIST digits (2s vs 5s) using RBF kernels
• Applied SVD for dimensionality reduction and identified the minimum dimension with >90% accuracy
• Evaluated tradeoff between model performance and computational cost

Neural Network with Backpropagation for Digit Recognition
Machine Learning — MATLAB

• Trained a neural network with one hidden layer (sigmoid activations) using stochastic gradient descent
• Implemented full backpropagation from scratch for 10-class MNIST classification
• Recorded error convergence and visualized performance using confusion matrices

Beam Deflection Simulation using Finite Differences
Numerical Methods — MATLAB

• Solved the Euler-Bernoulli beam equation using finite difference methods
• Modeled beam deflection under uniform, sinusoidal, and point loads with grid refinement
• Analyzed numerical accuracy and matrix condition numbers to assess stability

MEGL Math Research Team — George Mason University

• Collaborating on a research project exploring Graphons and Network Dynamical Systems under faculty supervision
• Investigating graph limits and continuum models to understand large-scale network behavior and their dynamic properties
• Contributing to simulations, literature reviews, and theoretical analysis aimed at potential publication

Independent Literature Review: Machine Learning for Disease Prediction

• Conducted a detailed review of recent (2020–2024) literature evaluating the accuracy of ML algorithms in predicting diseases
• Compared performance of algorithms like Random Forest, SVM, and KNN on conditions such as heart disease, diabetes, and COPD
• Assessed algorithmic limitations and proposed future directions for implementing ML in clinical decision support