Luís Freire

I am a Computer Science student with a big interest in the world of AI and specially in Deep Learning.

During my studies I have studied about many different topics in this realm and would love to deep-dive in one of them during the graduate program.

Education

M.Sc.Eng. Computer Science and Engineering

Technical University of Denmark (DTU)

Sep 2023 - Aug 2025

B.Eng. Software Engineering

VIA University College

Aug 2018 - Feb 2022

Courses
Elements of AI - Introduction & Building AI

University of Helsinki

Feb - March 2021

Traits

  • Eager to learn
  • Team player
  • Creative thinker
  • Brings the good mood

Languages

  • Portuguese
  • English
  • Spanish
  • Danish
  • French
  • Native
  • Proficient
  • Fluent
  • Interm.
  • Beginner
Work Experience

Student Worker

Associate Engineer

the LEGO Group

Sep 2023 - Present

Feb 2022 - Sep 2023

  • - Improved my knowledge on handling large amounts of data whilst working on a RESTful aplication in ABAP Cloud (SQL, ABAP)
  • - Focused on extending automation in CI/CD pipelines and drastically reduced deployment time (Node.js)
  • - Created a TypeScript library for reusable code and components improving code maintainability (SAPUI5)
  • - Developed a workflow in Databricks to automate the extraction and transformation of data (Python, Spark)
  • - Enhanced an internal Node package by extending support to use a CAP Node.js API as a proxy.

Back-end Developer Intern

Swap Language ApS

Sep 2020 - Feb 2021

  • - Worked mostly within the .NET Core environment (C#)
  • - Developed my understanding in cloud computing (Azure, Kubernetes, Docker)
  • - Designed and developed two microservices, one for Data Analysis and another, for sending Emails (MSSQL, CosmosDb)
  • - Learned more about QA practices, focusing on integration and unit testing
Relevant Courses & Projects

Deep Learning

Course Github Project Report
Course at DTU that provided a detailed understanding of Deep Learning both in thoery and practice. The topics were devided in the following modules:
  • - Feed Forward Neural Networks
  • - Convolutional Neural Networks
  • - Transformers, Recurrent Neural Networks & LSTM
  • - GANs, VAEs and Flow models
  • - Reinforcement Learning
My team's project focused on PINNs (Physics-Informed Neural Networks) and our goal was to simulate the behaviour of small wave while a body was placed in the water in a given snapshot. More information can be found in the project report.

Advanced Machine Learning

This course provided knowledge of current research topics in generative modeling, including handling issues of identifiability and non-trivial data such as graphs and it is the most advanced course regarding ML at DTU.
  • - 3 modules: deep generative models, geometric representations, and graph neural networks
  • - Focused on the theoretical foundations and mathematical model components
  • - Small project per module focused on the implementation of the theory learned without the use of libraries

AI and Multi-Agent Systems

This one primarily focused on topics within automated planning and multi-agent systems, but also addressed other areas of AI (e.g. problem-solving by searching, knowledge representation and reasoning with logical agents).

Here I was involved in a tournament where we developed intelligent agents that could communicate in a simulated environment. Unfortunately, the repository is private, but I would be happy to share more details if needed.
Outreach

ValHacks

Jun 2024

1-day ML Hackathon

The goal was to compress large audio files and classify unlabeled sounds (e.g., babies snoring, crying) into predefined labels. We explored Active Learning and pre-trained models, but faced data pre-processing issues and while some teams achieved good audio compression, no working model was ultimately developed given the time contraints. It was nonetheless a great experience where I learned about the challenges of working with audio data.