I'm a recent Master's graduate in Applied Computer Science from Dalhousie University with a passion for building intelligent systems and scalable applications. My work spans machine learning, backend engineering, and open-source blockchain development.

• Contributing to the Starknet blockchain ecosystem with production-grade Rust code
• Exploring deep learning architectures for audio processing and computer vision
• Building full-stack applications that solve real-world problems

Languages: Python, Rust, C++, Java, TypeScript/JavaScript, SQL, Bash

ML/AI: PyTorch, TensorFlow, Scikit-learn, Transformers, OpenCV, Pandas, NumPy

Backend: Flask, FastAPI, Spring Boot, Node.js, Express.js, GraphQL, gRPC

Frontend: React, Next.js, Vue.js, Tailwind CSS, Flutter, React Native

Infrastructure: Docker, Kubernetes, AWS, GCP, Azure, PostgreSQL, MongoDB, Redis

Comprehensive benchmarking framework comparing 8+ deep learning architectures and DSP algorithms for speech enhancement, achieving 18% SNR improvement and +0.4 PESQ gain.

End-to-end ML pipeline with Flask API, achieving R² of 0.87 and serving predictions at sub-800ms latency with Redis caching.

Fine-tuned BERT model on 100K+ tweets with full-stack deployment handling 1000+ tweets/minute in real-time.

Computer vision pipeline processing 720p video streams at 30+ FPS with 65% reduction in false positives.

• ML Engineer Intern @ Kintu Designs: Built production ML pipeline processing 55K+ records with sub-second inference, reducing manual preprocessing by 80%
• Open Source Contributor @ Starknet: Contributing to 6+ blockchain repositories (1000+ stars), patched critical security vulnerabilities, improved RPC performance by 25%

💡 Always open to collaborating on interesting projects in ML, distributed systems, and blockchain technology!