Ricardo P. M. Cruz

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

Ricardo P. M. Cruz received a B.S. degree in Computer Science and an M.S. degree in Applied Mathematics, both from the University of Porto, Portugal. Since 2015, he has been a researcher at INESC TEC, working in machine learning with a particular emphasis on computer vision. He earned his Ph.D. in Computer Science in 2021 with a special emphasis on computer vision and deep learning. After this, he was a post-doctoral researcher on autonomous driving under the THEIA research project. Currently, Cruz is an Assistant Professor at the Faculty of Engineering of the University of Porto. He works on a wide range of machine learning topics, with particular emphasis on theoretical aspects of deep learning and computer vision – with 20+ publications and 100+ citations in such topics as: • adapting ranking models for class imbalance; • making convolutional neural networks invariant to background; • making them faster by adjusting the computational effort to each image; • losses for ordinal regression.

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

Publications

h-index:
Google ScholarScopusWeb of Science
754

Sources for the following metrics: • Impact Factor (IF) as reported by the journal's webpage. • SJR rank quartiles are from Scimago and relate to the subject category closest to machine learning (not necessarily the best quartile). • CORE rank is from ICORE for whatever last year is available for that conference. • Citation counts come from Crossref. Last update: 2025-02-16

Supervisions

MSc Dissertation

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
Airton Tiago, "Data Augmentation for Ordinal Data", FEUP (with co-supervisor: Jaime Cardoso)
2023
Alankrita Asthana, "Iterative Inference for Point-Clouds", TUM
2023
Rafael Cristino, "Introducing Domain Knowledge to Scene Parsing in Autonomous Driving", FEUP (with co-supervisor: Jaime Cardoso)
2022
Pedro Silva, "Human Feedback during Neural Networks Training", FEUP (with co-supervisor: Tiago Gonçalves)
2022
Ana Bezerra, "Phishing Detection with a Machine Learning Net (Internship at E-goi)", FCUP (with co-supervisor: Joaquim Costa)

BSc Project

2024
João Monteiro, "Cross-vehicle collaboration using RGB cameras", FCUP (with co-supervisor: Celso Pereira)
2024
Diogo Mendes, "Automatic Recognition of Pig Activity in an Intensive Production System", FCUP (with co-supervisor: Nuno Lavado)
2024
Beatriz Sá, "Research on Deep Augmentation for Ordinal Regression", FCUP (with co-supervisor: Jaime S. Cardoso)
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 (with co-supervisor: Tiago Gonçalves)
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