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Hire Dr. Ricardo C.

Portugal

USD 30 /hr

I work in the intersection of mathematics and computer science, with particular focus on data mining and computer vision

Profile Summary

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 is frequently invited to teach at the Faculty of Engineering, University of Porto, where he earned a pedagogic award.

Subject Matter Expertise

• ★ Deep Learning
• ★ Computer Vision
• ★ Python
• ☆ Computer Science
• ☆ Data Analysis
• ☆ Data Mining
• ☆ Neural Networks
• ☆ Applied Mathematics
• ☆ R

Services

• Data & AI

Data & AI Predictive Modeling, Statistical Analysis, Image Processing, Image Analysis, Data Visualization

Work Experience

Post-doctoral Researcher

Universidade do Porto Faculdade de Engenharia

October 2021 - Present

Teacher Assistant

University of Porto

September 2018 - August 2022

Researcher

Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciência

September 2015 - June 2022

Researcher

Centre of Mathematics of the University of Porto

September 2014 - December 2014

Education

PhD in Computer Science

University of Minho, Aveiro, Porto

September 2016 - June 2021

MSc in Applied Mathematics

Faculty of Sciences, University of Porto

September 2014 - July 2015

BSc in Computer Science

Faculty of Sciences, University of Porto

September 2009 - July 2012

Certifications

• Certification details not provided.

Publications

BOOK CHAPTER

Ricardo P. M. Cruz, A. S. M. Shihavuddin, Md. Hasan Maruf, Jaime S. Cardoso (2024). Active Supervision: Human in the Loop .

Diana Teixeira e Silva, Ricardo P. M. Cruz (2024). Condition Invariance for Autonomous Driving by Adversarial Learning .

Filipe Campos, Francisco Gonçalves Cerqueira, Ricardo P. M. Cruz, Jaime S. Cardoso (2024). YOLOMM – You Only Look Once for Multi-modal Multi-tasking .

Active Supervision: Human in the Loop @inbook { author = {Ricardo P. M. Cruz and A. S. M. Shihavuddin and Md. Hasan Maruf and Jaime S. Cardoso}, title = {Active Supervision: Human in the Loop}, year = {2024}, url = {https://doi.org/10.1007/978-3-031-49018-7_38}, doi = {10.1007/978-3-031-49018-7_38} } .

Condition Invariance for Autonomous Driving by Adversarial Learning @inbook { author = {Diana Teixeira e Silva and Ricardo P. M. Cruz}, title = {Condition Invariance for Autonomous Driving by Adversarial Learning}, year = {2024}, url = {https://doi.org/10.1007/978-3-031-49018-7_39}, doi = {10.1007/978-3-031-49018-7_39} } .

YOLOMM – You Only Look Once for Multi-modal Multi-tasking @inbook { author = {Filipe Campos and Francisco Gonçalves Cerqueira and Ricardo P. M. Cruz and Jaime S. Cardoso}, title = {YOLOMM – You Only Look Once for Multi-modal Multi-tasking}, year = {2024}, url = {https://doi.org/10.1007/978-3-031-49018-7_40}, doi = {10.1007/978-3-031-49018-7_40} } .

Evaluating the Performance of Explanation Methods on Ordinal Regression CNN Models @inbook { author = {Javier Barbero-Gómez and Ricardo Cruz and Jaime S. Cardoso and Pedro A. Gutiérrez and César Hervás-Martínez}, title = {Evaluating the Performance of Explanation Methods on Ordinal Regression CNN Models}, year = {2023}, url = {https://doi.org/10.1007/978-3-031-43078-7_43}, doi = {10.1007/978-3-031-43078-7_43} } .

Ricardo Cruz, Javier Barbero-Gómez, Jaime S. Cardoso, Pedro A. Gutiérrez, César Hervás-Martínez (2023). Evaluating the Performance of Explanation Methods on Ordinal Regression CNN Models .

Interpretability-Guided Human Feedback During Neural Network Training @inbook { author = {Pedro Serrano e Silva and Ricardo Cruz and A. S. M. Shihavuddin and Tiago Gonçalves}, title = {Interpretability-Guided Human Feedback During Neural Network Training}, year = {2023}, url = {https://doi.org/10.1007/978-3-031-36616-1_22}, doi = {10.1007/978-3-031-36616-1_22} } .

Ricardo Cruz, Pedro Serrano e Silva, A. S. M. Shihavuddin, Tiago Gonçalves (2023). Interpretability-Guided Human Feedback During Neural Network Training .

Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> .

Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing @inbook { author = {Jaime Cardoso and Hien Van Nguyen and Nicholas Heller and Pedro Henriques Abreu and Ivana Isgum and Wilson Silva and Ricardo Cruz and et al.}, title = {Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing}, year = {2020}, url = {https://doi.org/10.1007/978-3-030-61166-8_30}, doi = {10.1007/978-3-030-61166-8_30} } .

Automatic Augmentation by Hill Climbing <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> .

(2019). Automatic Augmentation by Hill Climbing . Lecture Notes in Computer Science.

JOURNAL ARTICLE

Two-Stage Framework for Faster Semantic Segmentation @article { author = {Ricardo Cruz and Diana Teixeira e Silva and Tiago Gonçalves and Diogo Carneiro and Jaime S. Cardoso}, title = {Two-Stage Framework for Faster Semantic Segmentation}, journal = {Sensors}, year = {2023}, month = {mar}, url = {https://doi.org/10.3390/s23063092}, doi = {10.3390/s23063092} } . Sensors.

Ricardo Cruz, Diana Teixeira e Silva, Tiago Gon&#231;alves, Diogo Carneiro, Jaime S. Cardoso (2023). Two-Stage Framework for Faster Semantic Segmentation . Sensors.

Ricardo P. M. Cruz, Diana Teixeira e Silva, Tiago Filipe Sousa Gon&#231;alves, Diogo Carneiro, Jaime S. Cardoso (2023). Two-Stage Framework for Faster Semantic Segmentation . Sensors.

Ricardo Cruz, Tom&#233; Albuquerque, Lu&#237;s Rosado, Maria Jo&#227;o M. Vasconcelos, Tiago Oliveira, Jaime S. Cardoso(2023). Rethinking low-cost microscopy workflow: Image enhancement using deep based Extended Depth of Field methods . Intelligent Systems with Applications. 17. p. 200170. Elsevier {BV}

Rethinking low-cost microscopy workflow: Image enhancement using deep based Extended Depth of Field methods @article { author = {Tomé Albuquerque and Luís Rosado and Ricardo Cruz and Maria João M. Vasconcelos and Tiago Oliveira and Jaime S. Cardoso}, title = {Rethinking low-cost microscopy workflow: Image enhancement using deep based Extended Depth of Field methods}, journal = {Intelligent Systems with Applications}, volume = {17}, pages = {200170--200170}, year = {2023}, month = {feb}, url = {http://dx.doi.org/10.1016/j.iswa.2022.200170}, doi = {10.1016/j.iswa.2022.200170} } . Intelligent Systems with Applications.

Cardoso, JS, Cruz, R, Albuquerque, T (2023). Unimodal Distributions for Ordinal Regression. CoRR.

Unimodal Distributions for Ordinal Regression @article { author = {Cardoso, JS and Cruz, R and Albuquerque, T}, title = {Unimodal Distributions for Ordinal Regression}, journal = {CoRR}, year = {2023} }. CoRR.

Albuquerque, T, Rosado, L, Cruz, R, Vasconcelos, MJM, Oliveira, T, Cardoso, JS (2023). Rethinking low-cost microscopy workflow: Image enhancement using deep based Extended Depth of Field methods . Intelligent Systems with Applications.

Cruz, R, Silva, DTE, Goncalves, T, Carneiro, D, Cardoso, JS (2023). Two-Stage Framework for Faster Semantic Segmentation . SENSORS.

Albuquerque, T, Cruz, R, Cardoso, JS (2022). Quasi-Unimodal Distributions for Ordinal Classification . MATHEMATICS.

Quasi-Unimodal Distributions for Ordinal Classification @article { author = {Albuquerque, T and Cruz, R and Cardoso, JS}, title = {Quasi-Unimodal Distributions for Ordinal Classification}, journal = {MATHEMATICS}, year = {2022}, doi = {10.3390/math10060980} } . MATHEMATICS.

Albuquerque, T, Cruz, R, Cardoso, JS (2021). Ordinal losses for classification of cervical cancer risk . PEERJ COMPUTER SCIENCE.

(2019). Insulator visual non-conformity detection in overhead power distribution lines using deep learning . Computers & Electrical Engineering.

Binary ranking for ordinal class imbalance <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> . Pattern Analysis and Applications.

Cruz, R., Fernandes, K., Costa, J.F.P., Ortiz, M.P., Cardoso, J.S.(2018). Binary ranking for ordinal class imbalance . Pattern Analysis and Applications. p. 1-9.

Cruz, R, Fernandes, K, Costa, JFP, Ortiz, MP, Cardoso, JS (2018). Binary ranking for ordinal class imbalance . PATTERN ANALYSIS AND APPLICATIONS.

CONFERENCE PAPER

Two-Stage Semantic Segmentation in Neural Networks @inproceedings { author = {Silva, DTE and Cruz, R and Goncalves, T and Carneiro, D}, title = {Two-Stage Semantic Segmentation in Neural Networks}, year = {2023}, doi = {10.1117/12.2679881} } .

Silva, DTE, Cruz, R, Goncalves, T, Carneiro, D (2023). Two-Stage Semantic Segmentation in Neural Networks . FIFTEENTH INTERNATIONAL CONFERENCE ON MACHINE VISION, ICMV 2022.

Serrano e Silva, P, Cruz, R, Shihavuddin, ASM, Gon&#231;alves, T (2023). Interpretability-Guided Human Feedback During Neural Network Training . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

Cruz, R, Prates, RM, Simas, EF, Costa, JFP, Cardoso, JS (2021). Background Invariance by Adversarial Learning . 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR).

Cardoso, JS, Nguyen, HV, Heller, N, Abreu, PH, Isgum, I, Silva, W, Cruz, R, Amorim, JP, Patel, V, Roysam, B, et al. (2020). Correction to: Interpretable and Annotation-Efficient Learning for Medical Image Computing . Interpretable and Annotation-Efficient Learning for Medical Image Computing - Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings.

(2019). Averse Deep Semantic Segmentation . 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Fernandes, K, Cruz, R, Cardoso, JS (2018). Deep Image Segmentation by Quality Inference . 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018.

Cruz, R, Silveira, M, Cardoso, JS (2018). A Class Imbalance Ordinal Method for Alzheimer's Disease Classification . 2018 International Workshop on Pattern Recognition in Neuroimaging, PRNI 2018, Singapore, Singapore, June 12-14, 2018.

Cruz, R, Fernandes, K, Costa, JFP, Ortiz, MP, Cardoso, JS (2017). Combining Ranking with Traditional Methods for Ordinal Class Imbalance . ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II.

Perez Ortiz, M, Fernandes, K, Cruz, R, Cardoso, JS, Briceno, J, Hervas Martinez, C (2017). Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation . ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II.

Cruz, R, Fernandes, K, Costa, JFP, Cardoso, JS (2017). Constraining Type II Error: Building Intentionally Biased Classifiers . ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II.

Cruz, R, Fernandes, K, Costa, JFP, Ortiz, MP, Cardoso, JS (2017). Ordinal Class Imbalance with Ranking . PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2017).

Cruz, R, Fernandes, K, Cardoso, JS, Costa, JFP (2016). Tackling Class Imbalance with Ranking . 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN).

Cruz, R., Fernandes, K., Cardoso, J.S., Pinto Costa, J.F.(2016). Tackling class imbalance with ranking . Proceedings of the International Joint Conference on Neural Networks. 2016-October. p. 2182-2187.

PREPRINT

Deep learning-based system for real-time behavior recognition and closed-loop control of behavioral mazes using depth sensing @null { doi = {10.1101/2022.02.22.481410} } .

Deep learning-based system for real-time behavior recognition and closed-loop control of behavioral mazes using depth sensing <head> <META HTTP-EQUIV="Refresh" CONTENT="0;URL=/servlet/useragent"> </head> .

OTHER

Geros, AF, Cruz, R, de Chaumont, F, Cardoso, JS, Aguiar, P (2022). Deep learning-based system for real-time behavior recognition and closed-loop control of behavioral mazes using depth sensing .

BOOK

Combining ranking with traditional methods for ordinal class imbalance @book { author = {Cruz, R. and Fernandes, K. and Pinto Costa, J.F. and Ortiz, M.P. and Cardoso, J.S.}, title = {Combining ranking with traditional methods for ordinal class imbalance}, volume = {10306 LNCS}, year = {2017}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-85020890344&partnerID=MN8TOARS}, doi = {10.1007/978-3-319-59147-6_46} } .

Cruz, R., Fernandes, K., Pinto Costa, J.F., Ortiz, M.P., Cardoso, J.S.(2017). Combining ranking with traditional methods for ordinal class imbalance . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10306 LNCS. p. 538-548.

Cruz, R., Fernandes, K., Pinto Costa, J.F., Ortiz, M.P., Cardoso, J.S.(2017). Ordinal class imbalance with ranking . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10255 LNCS. p. 3-12.

P&#233;rez-Ortiz, M., Fernandes, K., Cruz, R., Cardoso, J.S., Brice&#241;o, J., Herv&#225;s-Mart&#237;nez, C.(2017). Fine-to-coarse ranking in ordinal and imbalanced domains: An application to liver transplantation . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10306 LNCS. p. 525-537.

Cruz, R., Fernandes, K., Pinto Costa, J.F., Cardoso, J.S.(2017). Constraining type II error: Building intentionally biased classifiers . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10306 LNCS. p. 549-560.