👋 Hello, I'm

Majed Althonayan

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I am a technology enthusiast based in London, England, specialising in Cyber Security, Machine Learning, and Software Development. Along this journey, I have obtained degrees, earned professional certifications and gained hands-on experience that has helped me develop my skills in systems analysis, problem solving, and building innovative solutions. I’m passionate about continuous learning and love staying at the forefront of development, mostly working with Python, Java, Typescript and Solidity. I enjoy tackling complex challenges and creating scalable, efficient applications. I’m excited to bring my expertise to forward-thinking teams and contribute to meaningful projects in today’s fast-moving landscape.

Education & Certifications

Secondary Education - GCSE's

12 GCSE's (A*-B).
Secondary Education - A Level's

A*AA in Mathematics, Computer Science and Chemistry.
Royal Holloway, University of London
Undergraduate Degree

BSc in Computer Science with a First Class Honours and an overall average of 81%.
Imperial College London
Postgraduate Degree

MSc in Computing (Security & Reliability) with a Distinction.
Certifications

Comptia Security+ and Google Cyber Security.

Projects

Blind Backrunning With Fully Homomorphic Encryption

This protocol utilises Fully Homomorphic Encryption (FHE) to enable searchers to blindly backrun private user transactions on decentralised exchanges on the Ethereum blockchain.

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Brain Graph Prediction Using Generative GNN's

This machine learning model leverages the capabilities of generative Graph Neural Networks (GNNs) to predict high-resolution brain MRIs from their low-resolution counterparts.

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Neural Network for California House Price Prediction

This project implements a custom, low-level implementation of a multi-layered neural network, built from scratch and incorporating a backpropagation algorithm to predict house prices in California. This model was developed entirely from scratch, without relying on any external machine learning libraries.

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Decision Tree for Location Detection Using Wi-Fi Signal Strengths

A decision tree algorithm designed to accurately determine indoor locations by analysing Wi-Fi signal strengths collected from a mobile phone. This approach leverages the unique signal profiles to provide precise location estimations within indoor environments.

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On-Chain Ticketing Service using NFTs

This on-chain ticketing service consists of three core components: a non-fungible token (NFT) contract that defines the ticket logic, a primary marketplace where users can create tickets by deploying new instances of the NFT contract, and a secondary marketplace that allows users to sell and place bids for re-sold tickets.

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PCAP Visualiser for Improved Network Traffic Analysis

A packet visualiser which enhances the traditional Wireshark user interface by providing advanced filtering and grouping capabilities, enabling users to conduct more efficient analysis of network traffic.

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Checkout some of my other projects on GitHub

Technologies & Applications

Python

Java

PHP

SQL

Linux

zsh

GitHub

HTML

Java Script

Next.js

Chat GPT

CSS

Chrome

Solidity

PyTorch

Numpy

VS Code

Scikit Learn

OpenCV

Wireshark

Figma

Metasploit

Firebase

Designed in Figma,

Coded in Visual Studio Code,

Built With Next.js and Tailwind CSS,

By Majed (me) 😁

A template of this portfolio can also be found on my GitHub