Hello, my name is

Akcel Graça.

Computer Engineering Student

Computer Engineering student at the University of Coimbra (graduating 2027) with a strong foundation in systems programming, full-stack development, and database engineering. Experienced in building high-concurrency architectures in C, full-stack applications in Java and React, machine learning pipelines in Python, and database-centric systems with PostgreSQL. Passionate about Cybersecurity — particularly Application Security and Cloud Security — and driven by the belief that understanding how systems are built is the first step to understanding how they can be broken.

01. S K I L L S

02. P R O J E C T S

Metro Mondego System

Full-stack database-centric system for managing an urban transit network with PostgreSQL backend and Python REST API. Features ticket management, wallet top-ups, real-time trip scheduling, fare promotions, and admin analytics for the Coimbra metro system.

  • PostgreSQL
  • Python
  • REST API
  • JWT
  • Docker

Hospital Emergency Simulator

Robust simulation of a hospital emergency service developed in C. Utilizes advanced IPC mechanisms (Named Pipes, Message Queues, Shared Memory) and synchronization (Mutexes, Condition Variables) to manage concurrent Patient, Triage, and Doctor processes in a Linux/POSIX environment.

  • C
  • Linux/POSIX
  • IPC
  • Multithreading
  • Shared Memory

Online Class & News Service

Distributed Client-Server system in C for class management and real-time news broadcasting. Implements a hybrid architecture (TCP for control, UDP for administration, IP Multicast for streaming) and uses Pthreads for concurrent processing.

  • C
  • TCP/UDP
  • IP Multicast
  • Pthreads
  • Socket API

Financial Management System (POOFS)

Java-based application for invoice and client management following OOP principles. Includes complex logic for dynamic VAT calculation (variable rates by location) and architecture designed with UML.

  • Java
  • OOP
  • UML
  • Design Patterns

Human Activity Recognition (HAR) Pipeline

End-to-end Machine Learning pipeline to classify human activities (e.g., walking, sitting) using inertial sensor data. Includes Feature Engineering, PCA, and data balancing with SMOTE.

  • Python
  • Scikit-Learn
  • Pandas
  • NumPy