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BSc in Computer Science & Engineering (CGPA 3.75) from AIUB, passionate about building intelligent systems using machine learning and deep learning, especially for real-world medical imaging and IoT applications. I transform data into meaningful insights through data analysis and visualization, with hands-on experience in C#, .NET, Python, Django, TensorFlow, and IoT systems.
I recently completed my BSc in Computer Science and Engineering from AIUB with a CGPA of 3.75, with a strong focus on machine learning, deep learning, and data science. My academic journey built a solid foundation in algorithms, data structures, and AI-driven problem solving, actively applied in real-world research and industry projects.
I am particularly enthusiastic about leveraging ML models to extract meaningful insights and build intelligent systems. I am proficient in Python, Java, C#, and C++ with hands-on experience in TensorFlow, Scikit-learn, Django, and .NET. From AI-based medical imaging solutions to scalable IoT applications, I enjoy transforming ideas into impactful technology. Let's connect and build together!
Domains I'm actively exploring and building in.
Technical depth and collaborative strengths.
Languages, frameworks & libraries I use regularly.
Recognitions and roles that highlight my journey.
Institutions that shaped my technical foundation.
Graduated with a CGPA of 3.75, focusing on algorithms, data science, machine learning, and software engineering. Conducted research in medical imaging and AI-based solutions. Achieved Excellent Performance Award in Science Poster Contest Spring 2022–23.
Completed Higher Secondary with a focus on core science subjects. Engaged in advanced lab work and problem-solving exercises; served as Organizing Secretary of the College Debating Club.
Completed Secondary School with strong marks in science. Active in extracurriculars including the Debate Club (General Secretary), Science Club volunteer, and community events.
A curated selection of builds across ML, systems, and software engineering.
Non-invasive AI system for estimating cattle body weight from images using deep learning regression models. Eliminates physical weighing stress, supporting scalable herd management and precision livestock farming through automated visual weight assessment.
📄 Case Study
Enterprise AI-powered dustbin fill-level monitoring system for 12+ railway stations. Built the EfficientNetV2 ML model, curated a 5,000+ image dataset, and engineered the full Django + Celery backend. Achieves 94% inference accuracy with 65ms latency.
📄 Case Study
AI-powered cattle identification system using muzzle biometrics. Integrated YOLOv8 for 98.7% detection accuracy and trained EfficientNetV2 classifier achieving 92.5% identification accuracy across 150+ unique animals with sub-50ms inference.
📄 Case Study
AI-powered aquaculture monitoring platform for real-time water quality tracking. Engineered MQTT IoT ingestion pipeline, ML inference for dissolved oxygen and ammonium prediction (scikit-learn + PyTorch), and Celery-based background processing with FCM push alerts.
📄 Case Study
Enterprise task management system with role-based access control, 3-tier architecture, and efficient workflow tracking for teams.
GitHub
Robust R-based data preprocessing pipeline for heart disease prediction-cleaning, transformation, normalization, outlier detection, and dataset balancing.
GitHub
NLP-based topic modeling and sentiment analysis on Aljazeera news data using LDA and VADER for real-time insight extraction.
GitHub
2D arcade game in C++ with OpenGL GLUT, featuring dynamic gameplay mechanics and three progressively challenging levels.
GitHub
Responsive e-commerce website with sleek design, seamless shopping cart flow, and efficient admin product management panel.
GitHub
ML-powered system predicting loan eligibility using Decision Tree and Naive Bayes classifiers with robust missing-value handling.
GitHub
Uses TF-IDF and Cosine Similarity to suggest films based on genres, keywords, cast, and user favorite history.
GitHub
Classic game featuring player vs. AI with adjustable difficulty levels, registration system, and win/loss statistics tracking.
GitHub
Manages product display, inventory, shopping cart, and shop owner registration for a seamless retail user experience.
GitHub
App for finding blood donors and managing user info-featuring registration, a searchable donor list, and password recovery.
GitHub
Recommends next-semester courses based on completed coursework, with student input interface and downloadable output.
GitHub
C# / SQL .NET app managing a bus transportation system-customer registration, verification, booking, and admin panel.
GitHub
Java Windows app for managing car inventory with admin and customer management roles; data stored in text-file persistence.
GitHubA record of my professional journey and contributions.
Academic work contributing to the field of medical AI.
This research proposes a deep learning-based fusion model integrating MobileNetV2 and EfficientNetB0 for multi-class brain tumor diagnosis using MRI images. The model leverages MobileNetV2's lightweight efficiency and EfficientNetB0's representational strength to achieve robust feature extraction while maintaining computational feasibility. The fusion approach achieved 95.33% validation accuracy with precision, recall, and F1-score of 96%, demonstrating strong performance for real-world clinical applications.
✦ Completed · Under Review for Journal Submission
I'm open to full-time roles, research collaborations, and exciting projects.
Feel free to reach out. I usually respond within 24 hours.
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