My Projects

A collection of projects I've worked on, from web applications to system tools and everything in between. Each project represents a challenge solved and skills developed.

TripTracker

πŸ“… Jul 2025
Finished
Kotlin Android Cloud Figma Full-Stack

TripTracker – A Social Exploration Companion

TripTracker is a mobile app we built as a team to make exploring cities on foot more enjoyable and social. The idea was to go beyond maps and directions β€” to create curated walking itineraries around interesting spots, where users could record their own paths, share them, and discover new ones from the community.

With TripTracker, users can:

Discover walking routes created by others.

DynamoDB in Go

πŸ“… Jul 2025
Finished
Go

Implementation of DynamoDB in Golang

DynamoDB in Go

As part of a distributed systems course at EPFL, me and my group built Dynaster, a Dynamo-inspired key-value store written in Go. The idea was to recreate some of the concepts behind Amazon’s Dynamo β€” high availability, replication, and fault tolerance β€” but in a simplified, educational setting.

The project runs on top of Peerster, a decentralized messaging and storage system we had been developing throughout the semester. Dynaster adds a new layer on top of it to manage how data gets distributed, stored, and recovered when things go wrong.

HACKUPC24' WINNING PROJECT

πŸ“… Jul 2025
Finished
Go

TravelSkibidi - Awasrd winning Project for HackUPC 2024

Winners of the TravelPerk challenge at HackUPC in Barcelona, the biggest student Hackathon in Europe

This project was done in a team of 3, with Nicolas Font, and Jeremy Kun.

Fast GPU Traffic Classification

πŸ“… Jul 2025
Finished
CUDA DPDK C++ Kafka Nix Grafana

A high-throughput encrypted traffic classification system built using CUDA and DPDK, designed to process network flows at over 75 Gbps. Developed for real-time inference and cybersecurity research.

Overview

This project focuses on classifying encrypted network traffic in real-time using GPU acceleration. It combines DPDK for high-speed packet ingestion with a custom neural network inference engine written in CUDA. The system targets cybersecurity and traffic engineering applications where line-rate classification is essential.