Ricardo Cruz, PhD

 rpcruz@fe.up.pt  0000-0002-5189-6228  github.com/rpmcruz  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 is frequently invited to teach at the Faculty of Engineering, University of Porto, where he earned a pedagogic award.


Sources (last update: 2024-06-14): • Citation counts are from Crossref. • Impact Factor (IF) comes from each 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.



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


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
- 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)
Invited Auxiliary Professor, University of Porto (FEUP)
- OAT4001 & FACVC : Machine Learning
Invited Auxiliary Professor, University of Porto (FEUP)
- L.EIC003 : Programming Fundamentals (Python)
- L.EEC009 : Data Structures and Algorithms (C/C++)
Invited Teacher Assistant, University of Porto (FEUP)
- L.EIC003: Programming Fundamentals (Python)
- L.EIC009 : Programming (C/C++)
Research Assistant on Machine Learning and Computer Vision (INESC TEC)
- Research focus: re-thinking fundamentals about image classification and semantic segmentation (8+ publications)
- Some highlights: (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
- Contributed to workshops, Summer School on Computer Vision (VISUM), and other events
- Twice awarded "outstanding recognition" for organizing workshops and helping with the HPC infrastructure
Research Grant on Mathematical Modelling Research (Mathematics Center of the University of Porto, CMUP)
- Epidemiological models for HIV. A little of everything: from differential equations to stochastic simulations to cellular automata.


Bosch for Mobility: My students won Best New Participating Team in an autonomous driving competition
INESC TEC Outstanding Recognition Award: INESC TEC internal award, reason: maintenance of the HPC infrastructure
Pedagogic award (voted by students): University of Porto (FEUP)
Best paper and presentation: RECPAD national conference
INESC TEC Outstanding Recognition Award: INESC TEC internal award, reason: help organizing workshops
Kaggle Bronze Medal (competition) and Silver (engagement)

Last update: 2024-06-14