Machine Learning Researcher

FCUP | TekPrivacy

Biography

I received my Master’s degree in Network and Information Systems Engineering from the Faculty of Sciences, University of Porto, in 2019. I am currently pursuing a Ph.D. in Computer Science at FCUP, with a dedicated research focus on “Automated Privacy-Preserving Strategies”.

In the context of machine learning and privacy, I have a particular interest in promoting the responsible and ethical use of data-driven technologies. My research focuses on secure data sharing using machine learning. Specifically, I have been studying meta-learning which plays an important role in finding optimal privacy-preserving solutions.

Education
  • PhD in Computer Science, 2024

    Faculty of Sciences, University of Porto

  • MSc in Network and Information Systems Engineering, 2019

    Faculty of Sciences, University of Porto

Experience

 
 
 
 
 
Data Science Researcher
Feb 2020 – Present Porto

Research topics include:

  • Federated Learning
  • De-identification Process
  • Privacy Enhancing Technologies (PETs)
  • Privacy-Utility Trade-off
  • Meta-learning
  • LLMs
 
 
 
 
 
Visiting Researcher
Mar 2023 – Jun 2023 Indiana, Unitate States

Developed projects:

 
 
 
 
 
Data Scientist Trainee
Oct 2018 – Sep 2019 Porto

MSc thesis - Antecipation of Services Perturbance

  • Classification tasks
  • Imbalance domains
  • Sliding windows
  • Big data

Interests

Machine Learning

  * Supervised Learning
  * Imbalanced Domains
  * Federated Learning
  * Meta-learning
  * Generative AI

Open Data

  * EDA
  * Record Linkage
  * Privacy-Utility trade off

Data Privacy

* De-identification process
* Privacy-preserving techniques
* Synthetic Data
* Privacy risks

Accomplish­ments

Best Paper Award
With the paper A Three-Way Knot: Privacy, Fairness, and Predictive Performance Dynamics

Projects

ε-PrivateSMOTE

ε-PrivateSMOTE

Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control

Hands-on data de-identification

Hands-on data de-identification

Practical course covering the basic principles of the data de-identification process.

Supervision

Current
  • Carolina Trindade (PhD) - Automated Reconstruction Attacks
  • Cristina Pêra (MSc) - Membership Inference Attacks in Vertical Federated Learning
  • Tiago Eusébio (MSc) - Automatic Detection of Personal Information in Tabular Data
Alumni
  • Miguel Ramos (MSc) - Advancements in Vertical Federated Learning: Utility Preservation with a Lightweight Model
  • Pedro Santos (MSc) - AI-Powered visual Privacy and Labeling
  • Carolina Trindade (MSc) - Identity Disclosure in Synthetic Data
  • Gustavo Pereira (MSc) - Automated Algorithm for Privacy Preservation of Data

Contact