Ricardo Cruz

 ricardo.pdm.cruz@gmail.com  rpmcruz  0000-0002-5189-6228  PDF

Ricardo Cruz has worked 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. He is a Post-doc Researcher on autonomous driving at the Faculty of Engineering, University of Porto, and he has been a researcher at INESC TEC since 2015, where his research earned him the computer science PhD in 2021. He has a BSc in computer science and a MSc in applied mathematics. He has frequently been an invited professor at the Faculty of Engineering, University of Porto, where he earned a pedagogic award.

Education

2021
PhD in Computer Science
Joint degree University of Porto, Minho and Aveiro
2015
M.Sc. in Mathematical Engineering
Faculty of Sciences, University of Porto
2012
B.Sc. in Computer Science
Faculty of Sciences, University of Porto

Work Experience

2023/07–present
Post-doctoral Researcher on Autonomous Driving INESC TEC [in partnership with Bosch]
2021/09–2023/06
Post-doctoral Researcher on Autonomous Driving University of Porto (FEUP) [in partnership with Bosch]
Collaboration between the University of Porto and Bosch Car Multimedia to improve autonomous driving perception. I developed frameworks for object detection using camera and LiDAR (2D discretization and raw point-clouds), published new methods for efficient semantic segmentation and ordinal regression, supervised six master's theses, four bachelor's projects, and other team members, responsible for the HPC infrastructure (using Slurm).
2023/09–2024/02
Invited Auxiliary Professor University of Porto (FEUP)
Courses on Machine Learning (theoretical lessons): - OAT4001 , FACVC
2021/09–2022/08
Invited Auxiliary Professor University of Porto (FEUP)
Courses: Programming Fundamentals (practical lessons in Python): - L.EIC003 , Data Structures and Algorithms (practical lessons in C/C++): L.EEC009
2018/09–2021/08
Invited Teacher Assistant University of Porto (FEUP)
Courses: Programming Fundamentals (practical lessons in Python): L.EIC003, Programming (practical lessons in C/C++): L.EIC009
2015/09–2021/08
Research Assistant on Machine Learning and Computer Vision INESC TEC
Research focus: re-thinking fundamentals about image classification and semantic segmentation (8+ publications), in particular (1) a method for background invariance using adversarial training, (2) new losses that minimize absolute trade-offs between Type 1 and 2 errors instead of relative trade-offs, (3) using backpropagation also for inference to refine existing outputs, (4) deploying learning-to-rank methods for class imbalance. During the period, I contributed to workshops, the Summer School on Computer Vision (VISUM), and other events, and was twice awarded "outstanding recognition" for organizing workshops and helping with the HPC infrastructure.
2014/09–2014/12
Research Grant on Mathematical Modelling Research Mathematics Center of the University of Porto (CMUP)
Epidemiological models for HIV: differential equations, stochastic simulations, cellular automata.

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: 2024-11-09

#YearPaperCitationsIFSJR RankCORE Rank
12024[ACCEPTED] J. Barbero-Gómez, R. Cruz, J. Cardoso, P. Gutiérrez, C. Hervás-Martínez, "CNN Explanation Methods for Ordinal Regression Tasks", Elsevier Neurocomputingjournal5.5Q1
22024[3RD ROUND] J. Cardoso, T. Albuquerque, R. Cruz, "Unimodal Distributions for Ordinal Regression", IEEE Transactions on Artificial Intelligencejournaln/aQ1
32024[2ND ROUND] R. Cristino, R. Cruz, J. Cardoso, "Learning Ordinality in Semantic Segmentation", IEEE Accessjournal3.4Q1
42024[SUBMITTED] D. Teixeira, R. Cruz, "Quantifying How Deep 3D Representations Promote Label Efficiency", Elsevier Neurocomputingjournal5.5Q1
52024[SUBMITTED] R. Cruz, J. Cardoso, "Navigating the Landscape of Deep Ordinal Methods: An In-Depth Review", IEEE Transactions on Neural Networks and Learning Systemsjournal10.2Q1
62024A. Bezerra, I. Pereira, M. Rebelo, D. Coelho, D. Oliveira, J. Costa, R. Cruz, "A case study on phishing detection with a machine learning net", Springer International Journal of Data Science and Analyticsjournal03.4Q2
72024C. Pereira, R. Cruz, J. Fernandes, J. Pinto, J. Cardoso, "Weather and Meteorological Optical Range Classification for Autonomous Driving", IEEE Transactions on Intelligent Vehiclesjournal114Q1
82023F. Campos, F. Cerqueira, R. Cruz, J. Cardoso, "YOLOMM – You Only Look Once for Multi-modal Multi-tasking", Iberoamerican Congress on Pattern Recognition 2023 (CIARP)conference0C
92023D. e Silva, R. Cruz, "Condition Invariance for Autonomous Driving by Adversarial Learning", Iberoamerican Congress on Pattern Recognition 2023 (CIARP)conference0C
102023R. Cruz, A. Shihavuddin, M. Maruf, J. Cardoso, "Active Supervision: Human in the Loop", Iberoamerican Congress on Pattern Recognition 2023 (CIARP)conference0C
112023P. Serrano e Silva, R. Cruz, A. Shihavuddin, T. Gonçalves, "Interpretability-Guided Human Feedback During Neural Network Training", Iberian Conference on Pattern Recognition and Image Analysis 2023 (IbPRIA)conference1C
122023J. Barbero-Gómez, R. Cruz, J. Cardoso, P. Gutiérrez, C. Hervás-Martínez, "Evaluating the Performance of Explanation Methods on Ordinal Regression CNN Models", International Work-Conference on Artificial Neural Networks 2023 (IWANN)conference0n/a
132023R. Cruz, D. Silva, T. Gonçalves, D. Carneiro, J. Cardoso, "Two-Stage Framework for Faster Semantic Segmentation", MDPI Sensorsjournal13.4Q2
142023D. Teixeira e Silva, R. Cruz, T. Gonçalves, D. Carneiro, "Two-stage semantic segmentation in neural networks", Fifteenth International Conference on Machine Vision (ICMV 2022)conference0n/a
152023T. Albuquerque, L. Rosado, R. Cruz, M. Vasconcelos, T. Oliveira, J. Cardoso, "Rethinking low-cost microscopy workflow: Image enhancement using deep based Extended Depth of Field methods", Elsevier Intelligent Systems with Applicationsjournal0n/aQ1
162022T. Albuquerque, R. Cruz, J. Cardoso, "Quasi-Unimodal Distributions for Ordinal Classification", MDPI Mathematicsjournal22.3Q2
172021T. Albuquerque, R. Cruz, J. Cardoso, "Ordinal losses for classification of cervical cancer risk", PeerJ Computer Sciencejournal153.8Q1
182021R. Cruz, R. Prates, E. Simas Filho, J. Pinto Costa, J. Cardoso, "Background Invariance by Adversarial Learning", 2020 25th International Conference on Pattern Recognition (ICPR)conference1B
192019R. Cruz, J. Pinto Costa, J. Cardoso, "Averse Deep Semantic Segmentation", 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)conference1C
202019R. Prates, R. Cruz, A. Marotta, R. Ramos, E. Simas Filho, J. Cardoso, "Insulator visual non-conformity detection in overhead power distribution lines using deep learning", Elsevier Computers & Electrical Engineeringjournal474.0Q1
212019R. Cruz, J. Pinto Costa, J. Cardoso, "Automatic Augmentation by Hill Climbing", International Conference on Artificial Neural Networks 2019 (ICANN)conference1C
222018K. Fernandes, R. Cruz, J. Cardoso, "Deep Image Segmentation by Quality Inference", 2018 International Joint Conference on Neural Networks (IJCNN)conference10B
232018R. Cruz, M. Silveira, J. Cardoso, "A Class Imbalance Ordinal Method for Alzheimer’s Disease Classification", 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI)conference0n/a
242018R. Cruz, K. Fernandes, J. Costa, M. Ortiz, J. Cardoso, "Binary ranking for ordinal class imbalance", Springer Pattern Analysis and Applicationsjournal23.7Q2
252017R. Cruz, K. Fernandes, J. Pinto Costa, J. Cardoso, "Constraining Type II Error: Building Intentionally Biased Classifiers", Lecture Notes in Computer Scienceconference3B
262017M. Pérez-Ortiz, K. Fernandes, R. Cruz, J. Cardoso, J. Briceño, C. Hervás-Martínez, "Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation", Lecture Notes in Computer Scienceconference1B
272017R. Cruz, K. Fernandes, J. Pinto Costa, M. Pérez Ortiz, J. Cardoso, "Combining Ranking with Traditional Methods for Ordinal Class Imbalance", Lecture Notes in Computer Scienceconference8B
282017R. Cruz, K. Fernandes, J. Pinto Costa, M. Ortiz, J. Cardoso, "Ordinal Class Imbalance with Ranking", Lecture Notes in Computer Scienceconference5B
292016R. Cruz, K. Fernandes, J. Cardoso, J. Pinto Costa, "Tackling class imbalance with ranking", 2016 International Joint Conference on Neural Networks (IJCNN)conference23B

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