Oriol Pujol Vila
- Full Professor of Computer Science and Artificial Intelligence
- Researcher in Machine Learning
- Universitat de Barcelona
- oriol_pujol@ub.edu
My research is focused on the design of Machine Learning algorithms. My long-term research is focused on understanding pattern formation, information compression, and prediction as a fundational component of weak emergence; understanding human decision making and human inductive bias for the creation of appropriate machine learning algorithms; and building trust in a human-centric and ethical development and deployment of AI.
I teach "Machine Learning" at the Fundamental Principles of Data Science master's program, "Digital Transformation" at the Strategic Security Management's master program, and "Advanced Algorithmics" in the undergraduate programm of Computer Science.
I am currently free from management duties.
Oriol Pujol Vila is a Full Professor of Computer Science and Artificial Intelligence at the department of Matemàtiques i Informàtica at Universitat de Barcelona. He holds a degree in Telecommunications Engineering from UPC and a PhD in Computer Science from UAB. He joined the University of Barcelona in 2005. His research focuses on the foundations of machine learning and its application to social challenges, including medical image analysis, ensemble learning, anomaly detection, sequential learning, and deep learning with limited data and multitask supervision. In recent years, his work has shifted toward trustworthy and human-centered AI, including uncertainty estimation, auditing opaque models, bias mitigation, and privacy preservation. His scientific output includes more than two hundred international publications in the field of machine learning and its applications. He has held academic leadership positions such as Head of Studies in Computer Science, Director of the Master’s Degree in Foundations of Data Science, Vice-Rector for Digital Transformation, and Dean of the Faculty of Mathematics and Computer Science at the University of Barcelona.
My current basic research lines are focused on the topic of online learning optimization strategies. My current goals are to design new optimization algorithms that allow machine learning techniques to work with low computational resources and to propose new computational models in the task of supervised learning.
My current research line in deep learning is focused in designing architectures that allow the use of weak supervisory signals for efficient learning of deep learning models in the presence small data sets. I am also interested in generative algorithms and the role of uncertainty for sampling plausible instances.
One of the topics I have been researching for a long time is ensemble learning. In particular Error Correcting Output Coding techniques.
My main area of application is computer vision, though I have applied machine learning methods in many other domains, such as finance, click-through-rate prediction, e-health, or medical imaging among others.
I am currently teaching Machine Learning at Foundamental Principles of Data Science master's program and also at the Artificial Intelligence master's program. I like to use Jupyter Notebooks and live coding sessions for teaching this subject. My goal is that students not only have the basic understanding of the subject but also know how to effectively apply it, and furthermore, are able to code from scratch most of the most well known machine learning techniques, e.g. support vector machines, deep learning algorithms, random forests, among others.
I teach agile methodologies for undergraduate Computer Science students. This includes Scrum, Kanban, and Lean principles. Besides these subjects basic skills for leadership, project, team, and time management are also taught. Two particular subjects I like to emphasize in this course are mediation and negotiation.
I taught Image Processing and Computational Photography.
M Ciprian, L Baldassini, L Peinado, T Correas, R Maestre, J A Rodriguez Serrano, O Pujol, J Vitrià, NIPS Workshop on Machine Learning for Spatio-temporal Forecasting, 2016.
S Seguí, O Pujol, J Vitria, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
P Casale, O Pujol, P Radeva, Pattern Recognition, 2014
C Gatta, E Puertas, O Pujol Pattern Recognition, 2011
O Pujol, D Masip, IEEE Transactions on Tattern Analysis and Machine Intelligence, 2009
MA Bautista, O Pujol, F De la Torre, S Escalera, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
S Escalera, O Pujol, P Radeva IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
S Escalera, DMJ Tax, O Pujol, P Radeva, RPW Duin IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
O Pujol, P Radeva, J Vitria IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006.
MA Bautista, A Hernández-Vela, S Escalera, L Igual, O Pujol, J Moya, V Violant, MT Anguera, IEEE transactions on cybernetics,2016.
P Casale, O Pujol, P Radeva Personal and Ubiquitous Computing, 2012
P Casale, O Pujol, P Radeva Iberian Conference on Pattern Recognition and Image Analysis,2011
JC Seabra, F Ciompi, O Pujol, J Mauri, P Radeva, J Sanches IEEE Transactions on Biomedical Engineering, 2011
Medical Imaging, Computer Assisted Diagnosis
User Authentication, Action Recognition
Traffic Sign Recognition, Industrial Vision
Click-through Rate, Time Series Forecasting
