MEng Computer Science at UCL
Hello there!
I'm Syed Ali Abbas, a Computer Science student at UCL
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About Me
Syed Ali Abbas
MEng Computer Science at UCL
Computer Science | AI Systems | Robotics | Applied ML
I am a Computer Science MEng student at University College London with a First Class average and a strong focus on AI systems engineering, applied machine learning, and robotics. I build end-to-end, production-ready platforms that combine rigorous engineering, modern AI techniques, and thoughtful user experience from Retrieval-Augmented Generation systems to global EdTech platforms.
2023-2024
Featured Projects
Flagship projects spanning AI systems, robotics, and full-stack development.
Sapiens Nova Academy
End-to-end EdTech platform with AI-powered learning assistant
Sapiens Nova Academy is a global education and innovation platform designed to prepare students for careers in technology and entrepreneurship. Built with Next.js, TypeScript, Tailwind CSS, featuring a Retrieval-Augmented Generation (RAG) assistant powered by LangChain, integrated Stripe payment processing, and PostgreSQL database with pgvector for semantic search. The platform supports multi-programme education delivery, secure enrollment management, and real-time payment tracking.
Systems Engineering Intern at HP
Production RAG pipeline for intelligent document processing
Designed and implemented a Retrieval-Augmented Generation pipeline for enterprise document understanding at Hewlett-Packard. Built with Python, LangChain, Llama models, and Hugging Face embeddings. The system features document ingestion, intelligent chunking, semantic retrieval, and response generation workflows. Containerized using Docker for reproducibility and deployment, with comprehensive evaluation strategies to assess model accuracy and grounding.
Autonomous Robotics Systems
Pick-and-place robot with trajectory planning and automation
Designed and implemented robotics systems including a pick-and-place robot using cubic polynomial trajectory planning, self-balancing and obstacle-avoiding robots, and Bluetooth-controlled systems. Built Arduino-based control systems with comprehensive sensor integration. Developed and installed automated water tap systems that reduced water wastage by approximately 30%. Projects combine mathematical modelling, control theory, and real-time embedded systems.
ML Computer Vision Systems
CNN-based image processing and neural texture synthesis
Implemented advanced machine learning systems for visual computing, including CNN-based image denoising using PyTorch, neural texture synthesis using Gram matrices, and object detection and segmentation pipelines. Applied advanced image processing techniques in MATLAB and PyTorch, combining theoretical understanding of convolutional architectures with practical implementation for real-world visual tasks.
