Ricardo P. M. Cruz

 rpcruz@fe.up.pt  rpmcruz  0000-0002-5189-6228  Scholar  PDF

Ricardo P. M. Cruz is an Assistant Professor at the Faculty of Engineering of the University of Porto and a researcher at INESC TEC. His work focuses on machine learning, particularly deep learning, and computer vision. He received a B.Sc. degree in Computer Science (2012), an M.Sc. degree in Mathematical Engineering (2015), both from the University of Porto, and a Ph.D. degree in Computer Science (2021) jointly from the University of Porto, Aveiro and Minho. His topics cover transversal aspects of machine learning with applications to health and autonomous driving, detailed in over 20 publications with 100+ citations.

If you are a student that would like to do research for a project, masters dissertation or PhD, drop me an email.

Publications

Sources for the following metrics: • Impact Factor (IF) as reported by the journal's webpage. • SJR rank quartiles are from Scimago and best quartile is chosen when multiple categories exist. • CORE rank is from ICORE for whatever last year is available for that conference. • Citation counts come from Crossref. Last update: 2025-03-03

Supervisions

MSc Dissertation

current
Rúben Monteiro, "Intelligent 3D Interaction", FEUP (main supervisor: Rui Rodrigues, co-supervisor: R. Cruz)
current
João Ricardo Ramos Alves, "Self-supervised Occupancy Networks in Autonomous Driving (Bosch internship)", FEUP
current
Luís Paulo da Rocha Miranda, "Unsupervised Active Learning: Which Frames are Most Important in Autonomous Driving?", FEUP
current
Bruno Vieira Dias, "Trust or not to trust: When to trust the label prediction", FEUP
current
Alexandre Ferreira Nunes, "Semi-supervised Learning on 2D Projections for Autonomous Driving", FEUP
current
Sofía Lucía, "Ordinal losses for range estimation in autonomous driving", FEUP
2024
Diana Teixeira Silva, "Quantifying How Deep 3D Representations Promote Label Efficiency", FEUP
2024
Francisco Gonçalves Cerqueira, "Exploring Label Efficiency with Semi-Supervision and Self-Supervision Methods", FEUP
2024
Airton Tiago, "Data Augmentation for Ordinal Data", FEUP
2023
Alankrita Asthana, "Iterative Inference for Point-Clouds", TUM
2023
Rafael Cristino, "Introducing Domain Knowledge to Scene Parsing in Autonomous Driving", FEUP
2023
José Guerra, "Uncertainty-Driven Out-of-Distribution Detection in 3D LiDAR Object Detection for Autonomous Driving (Internship at Bosch Car Multimedia)", FEUP
2022
Pedro Silva, "Human Feedback during Neural Networks Training", FEUP
2022
João Silva, "Environment Detection for Railway Applications based on Automotive Technology (Internship at Continental)", FEUP
2022
Ana Bezerra, "Phishing Detection with a Machine Learning Net (Internship at E-goi)", FCUP

BSc Project

current
Diogo Venade, "How Robust are Ordinal Regression Methods against Adversarial Attacks?", FEUP
current
Bruno Ferreira, "Automatic Recognition of Pig Behavior", FEUP
current
Isabel Sá, "Automatic Recognition of Pig Behavior", FEUP
2024
João Monteiro, "Cross-vehicle collaboration using RGB cameras", FCUP
2024
Diogo Mendes, "Automatic Recognition of Pig Activity in an Intensive Production System", FCUP
2024
Beatriz Sá, "Research on Deep Augmentation for Ordinal Regression", FCUP
2024
Eliandro Melo, "Resource Efficiency using Deep Q-Learning in Autonomous Driving", FCUP
2024
Ivo Duarte Simões, "Resource Efficiency using PPO in Autonomous Driving", FCUP
2023
Diana Teixeira Silva, "Condition Invariance for Autonomous Driving by Adversarial Learning", FEUP
2022
Diana Teixeira Silva, "Semantic Segmentation in Neural Networks using Iterative Visual Attention", FEUP
2022
Filipe Campos, Francisco Cerqueira, Vasco Alves, "Mobile App using Object Detection for Car Driving", FEUP
2022
Bruno Gomes, Rafael Camelo, "Internship at ANO", FEUP

Internship

2024 July
Isabel Gomes de Sá, "Making Sense of Ordinal Images Without Labels", INESC TEC