My research is focused on the design of Machine Learning algorithms. In particular supervised learning optimisation strategies for large scale data sets.
I teach "Agile Methodologies for Software Development", and "Machine Learning" at the Fundamental Principles of Data Science master's programm and "Introduction to Machine Learning" at the Artificial Intelligence master's joint programm involving UPC, URV, and UB.
I am currently the vice-president for Digital Transformation at Universitat de Barcelona and Principal Investigator of the Vision and Computational Learning consolidated research group (SGR).
Oriol Pujol Vila is tenured associate professor in Computer Science and Artificial Intelligence at the department of Matemàtiques i Informàtica at Universitat de Barcelona. He obtained the degree in Telecomunications Engineering in 1998 from the Universitat Politècnica de Catalunya (UPC). The same year, he joined the Computer Vision Center and the Computer Science Department at Universitat Autònoma de Barcelona (UAB). In 2004 he received the Ph.D. in Computer Science at the UAB with a work in deformable models, fusion of supervised and unsupervised learning and intravascular ultrasound medical image analysis. In 2005 he joined the Dept. of Matemàtica Aplicada i Anàlisi at Universitat de Barcelona (UB) where he became tenured associate professor. He currently leads the Vision and Computational Learning consolidated research group (SGR). He has published more than one hundred and fifty articles in machine learning, computer vision, and their applications. He has more than eighteen years in knowledge transference in data analysis projects is fields such as finance, health, marketing, wearable sensors, among others. He served as director of Computer Science undergraduate studies, director of the postgraduate courses on Data Science and Big Data, and director of the official master's program in Fundamental Principles of Data Science. He is currently vice-president for Digital Transformation 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