Expertise

Artificial Intelligence & Machine Learning

Expertise in developing and deploying AI solutions, including RAG chatbots, predictive modeling, and deep learning applications.

PythonScikit-learnTensorFlowPyTorchRAG

Natural Language Processing

Skilled in text cleaning, feature extraction (TF IDF, stemming, lemmatization), and building models for tasks like job fraud detection and natural language interaction.

NLPRAGText CleaningTF IDFLemmatization

Computer Vision

Proficient in applying computer vision techniques such as contrastive learning, pattern extraction, and data augmentation to enhance detection systems and image classification.

Computer VisionContrastive LearningPattern ExtractionData AugmentationImage Classification

Data Engineering & Web Scraping

Experienced in leading large-scale web scraping operations using Python/Scrapy, ensuring high data accuracy and optimizing crawl efficiency.

PythonScrapyWeb ScrapingData AccuracyData Extraction

Projects

Shape Extraction from Noisy and Pixelized Images

More accurate measurements from challenging image data.

Extracted shapes from heavily pixelized, noisy images using contour detection, noise reduction, and k-means clustering. Calculated areas, curvature, and angles while addressing distortions and removing excess corners for more accurate measurements.

Driver Monitoring System (Graduation Project)

Reduced model size by 95% (25M → 1.25M parameters) with SqueezeNet, enabling real-time inference.

Built a contrastive learning model for distracted driving detection, achieving high accuracy across varied driving scenarios. Applied targeted augmentation to improve robustness under low-light, varied postures, and changing environments.

Natural Language Processing Project

Achieved 98.3% accuracy.

Developed a job fraud detection model, achieving 98.3% accuracy with TF IDF and logistic regression. Improved feature extraction using stemming and lemmatization techniques.

Sport Images Classification

Achieved 93.1% accuracy on 650 sports test images.

Trained Xception, CNN, and ResNet models on 1,700 sports images with augmentation.

Timeline

2025

Researching and developing a Retrieval-Augmented Generation (RAG) chatbot at Reva Group to enable accurate natural-language interaction with rental data.

2025

Led the Area of Responsibility (AOR) for large-scale web scraping with Python/Scrapy at Reva Group, ensuring high data accuracy and reliability.

2023-2024

Designed and developed a RAG-based chatbot as the sole developer to streamline access to internal documentation during an internship at Valeo.

2024

Graduated with First-Class Honours in B.Sc. in Computer Science from the University of East London.

2024

Completed "Neural Networks and Deep Learning" certification.

2023-2024

Enhanced Occupant Monitoring System by analyzing video samples to identify and resolve detection issues, reducing false positives and improving reliability at Valeo.