Computer scientist with experience in Python development, GIS applications, full stack and AI-powered edge computing. Passionate about programming languages, free software and computer science in general
Computer scientist with experience in Python development, GIS applications, full stack and AI-powered edge computing. Passionate about programming languages, free software and computer science in general
Built Deep Learning models and web solutions for mobility analytics, including vehicle tracking and customer-facing LLM-powered chat systems. Did lots of Python scripting and tools for different purposes
Python development in different Deep Learning projects (OCR, vehicle tracking&counting, vehicle analytics)
Designed and consumed REST APIs; integrated external SOAP and GraphQL endpoints for OCR and vehicle‑detection services
Implemented real‑time inter‑service messaging with raw TCP/IP sockets alongside HTTP APIs
Web development using Flask
Worked with Docker and Docker Compose to deploy and manage containerized applications in development and edge environments
Delphi programming
NodeJS web development using Express.js & Next.js
Creation of a Web Chat for customers using an LLM API
Edge programming
Developed and maintained GIS applications and RESTful web services, integrating mapping technologies using Python and Java...
PyQGIS standalone programs & QGIS plugins
Scripting tasks using Python
Backend Web development using Django
Full Stack Web development using Java (Hibernate, Tomcat, SpringBoot..., JQuery, Bootstrap...)
Frontend‑to‑backend communication fully decoupled through RESTful APIs, enabling SPA and mobile clients
Member of DEFIB (FIB's students delegation)
Representative of students in different subjects during my bachelor's and master's degree
Member of the Faculty Board between 2018 and 2020
Member of the CACOCFFI, the commitee responsible for evaluating the academic performance of students during the mid phase of the Bachelor’s Degree in Informatics Engineering
In the current technological context, Large Language Models (LLM) such as GPT have experienced significant growth in popularity and applications, becoming a valuable tool for various audiences, including students. This project aims to explore and develop the integration of these powerful models into online learning platforms like Moodle, with the goal of creating educational tools that can enhance the quality and experience of learning.
Centrality measures have recently been proposed for social networks, like the FLTR. This proposal continues the experimental study of the influence threshold selection in this measure. Previous studies have obtained results for some social network. This work will expand the study analyzing different threshold selection mechanisms in synthetic social network models.