Nabeel Syed

Computer Systems Engineering · Software Systems · Applied AI

Building intelligent systems across software, AI, and embedded engineering.

Final-year Computer Systems Engineering student at Middlesex University Dubai focused on backend systems, AI-powered applications, local-first software, and hardware-integrated intelligent systems.

I build systems that combine software engineering, applied AI, and real-world problem solving.

My work ranges from AI-powered web platforms and local-first desktop applications to embedded automation systems and edge AI pipelines. I enjoy working across the full stack — from backend architecture and ML integration to frontend experiences and hardware/software interaction.

Current areas of interest:

• AI-integrated software systems
• Backend engineering and APIs
• Local-first and offline-capable applications
• Intelligent data systems and automation
• Embedded and edge computing
• Human-centered engineering

Currently building:

A bilingual desktop platform designed for Indian government schools with unreliable or no internet access. Built to operate fully offline with local storage and zero cloud dependency.

Solo Project

Desktop-based school management platform designed for low-connectivity environments. Handles attendance, student records, marks, and class promotion workflows entirely offline.

• Local SQLite database architecture
• Electron desktop application with React frontend
• Hindi/English bilingual support
• No cloud dependency or subscription model
• Optimized for low-resource school environments

Electron React TypeScript SQLite

Solo Project · Live Deployment

AI-assisted knowledge management system for storing, searching, and revising long-form content from videos, articles, and social media sources.

• Extracts and processes external content
• Generates summaries and revision quizzes
• Semantic search across saved knowledge
• Streaming AI responses using SSE
• Full-text search with PostgreSQL

Try Recall

React FastAPI Supabase Groq PostgreSQL

Solo Project

IoT-based dietary tracking platform combining computer vision, embedded hardware, and nutritional analysis.

The system identifies food items using AI while nutritional values are retrieved from a verified local database to reduce unreliable generated outputs.

• Raspberry Pi edge processing
• Load cell-based weight measurement
• Camera-assisted food recognition
• Local nutritional database (~8,900 foods)
• ML-assisted dietary trend analysis

• Edge AI
• Sensor fusion
• Hardware/software integration
• Local-first inference pipeline

Raspberry Pi 5 HX711 Flask Random Forest Ollama Gemini

Solo Project

Natural language interface for querying structured datasets locally.

Supports CSV and JSON analysis with AI-generated visualizations while prioritizing local inference using Ollama.

• Sandboxed Python execution
• Automated chart generation
• Local LLM inference pipeline
• Optional cloud fallback support
• Natural language querying over datasets

React FastAPI Ollama Pandas Matplotlib

Group Project · Team of 3

Health monitoring platform combining wearable biometrics, environmental sensing, and ML-driven health scoring.

• Built custom Kotlin Capacitor plugin for Health Connect integration
• Developed multi-source sensor data pipeline
• Integrated wearable and environmental data streams
• Implemented ML-based vitality scoring workflow

• Sensor fusion
• Edge-first architecture
• Real-time health monitoring
• Mobile + IoT integration

Capacitor Kotlin MQTT Flask Node.js Supabase

Solo Project

Embedded automation system controlling room lighting and blinds using ambient light and motion sensing.

• Real-time sensor processing
• Automatic blind adjustment
• Motion-triggered lighting control
• Low-power sleep operation
• Fail-safe control logic

STM32 Nucleo-F429ZI PIR Sensor Servo Motor Relay Module

• Edge AI systems
• Embedded intelligence
• AI-assisted automation
• Human-centered engineering
• Real-time systems
• Intelligent IoT infrastructure