Short Bio

Jordi Vitrià is a Full Professor at the University of Barcelona (UB), which he joined in 2007, and where he teaches an introductory course on Algorithms for Data Analysis and advanced courses on AI Ethics, Data Science and Deep Learning.

From April 2011 to January 2016 he served as UB’a Head of the Applied Mathematics and Analysis Department. He is now a member of the new Mathematics & Computer Science Department at UB. He is also the director of the Master in Fundamental Principles of Data Science and co-director of the Data Science and Artificial Intelligence Postgraduate course at UB.

His research, when personal computers had 128KB of memory, was originally oriented towards digital image analysis and how to extract quantitative information from them, but soon evolved towards computer vision problems. After a postdoctoral year at the University of California at Berkeley in 1993, he focused on Bayesian methods for computer vision methods. Now, he is leading a research group working in deep learning, machine learning and causal inference. He has authored more than 100 peer-reviewed papers and holds several international patents. He has directed 15 PhD theses in the area of machine learning and computer vision. He has been the leader of a large number of research projects at international and national level.

He has been always interested in connecting his academic work to the local technological ecosystem, and since 1993 he has been involved in a large number of projects to transfer academic knowledge to industrial production, medical diagnoses and media applications. He is now director of the DataScience@UB group technology transfer group that is supported by the Generalitat de Catalunya (TECNIO Center). During the last years the DataScience@UB group has been actively collaborating with companies such as Driving01, DegustaBox, Nestle, CorporateHealth, Eurecat, BBVA, IBM, TUV Rheinland, Telefonica, Vodafone, ICGC, Bodas.net, Given Imaging, correYvuela, Ajuntament de Barcelona, 7ideas, etc.

Public service and other activities

Member of the LERU team of experts on Responsible AI. The team’s missiom is to identify what responsible AI means for 24 EU leading universities and map the impact of AI on our institutions.
Organizing Committee, KDD 2024.
Member of the Executive Board of the Institute of Marthematics, Universitat de Barcelona.
Member of the Advisory Committee for Unique Infrastructures, Ministerio de Ciencia e Innovación, Spain.
Member of the Advisory Committee for Next Generation EU Strategy at the Universitat de Barcelona.
Member of the Advisory Board, Universitat de Barcelona.
Member of the Advisory Board of the AI4ES project.
Member of the Advisory Board of the Euhubs4data project.

Highlights

February 2024:

Paula G. Duran, Pere Gilabert, Santi Seguí, and Jordi Vitrià. 2024. Overcoming Diverse Undesired Effects in Recommender Systems: A Deontological Approach. ACM Trans. Intell. Syst. Technol. Just Accepted (February 2024). https://doi.org/10.1145/3643857

October 2023:

Pros, R., Vitrià, J. Exploiting causal knowledge during CATE estimation using tree based metalearners. ECML/PKDD’23 Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making, 2023.
Vitrià, Jordi, Oriol Pujol, and Pilar Dellunde. **Cerrando una brecha: una reflexión multidisciplinar sobre la discriminación algorítmica | Bridging a gap: A multidisciplinary reflection on algorithmic discrimination.** Daimon Revista Internacional de Filosofia 90 (2023): 63-80.
Laiz, P., Vitrià, J., Gilabert, P., Wenzek, H., Malagelada, C., Watson, A. J., & Seguí, S. (2023). **Anatomical landmarks localization for capsule endoscopy studies**. Computerized Medical Imaging and Graphics, 108, 102243.
Carós, Mariona, et al. **Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR data.** 2023 18th International Conference on Machine Vision and Applications (MVA). IEEE, 2023.
P Gilabert, C Malagelada, H Wenzek, J Vitrià, S Seguí, **Leveraging Embedding Information to Create Video Capsule Endoscopy Datasets** , 18th International Conference on Machine Vision and Applications (MVA). IEEE, 2023.
Gilabert, P., Vitrià, J., Laiz, P., Malagelada, C., Watson, A., Wenzek, H., & Segui, S. (2022). Artificial intelligence to improve polyp detection and screening time in colon capsule endoscopy. Frontiers in Medicine, 9, 1000726.

June 2023:

Arnau Quindós, Pablo Laiz, Jordi Vitrià, Santi Seguí, Self-supervised out-of-distribution detection in wireless capsule endoscopy images, Artificial Intelligence in Medicine, 2023, 102606, ISSN 0933-3657, https://doi.org/10.1016/j.artmed.2023.102606

May 2023: Invited Talk: AI is broken. Club Business Innovation & Technologies Esade Alumni.

May 2023: Invited Talk: El rol de la IA en la nueva medicina, Jornadas para Alta Dirección en Salud, San Telmo Business School, Sevilla. Organizado por Bioinformatics BCN, San Telmo Business School y Junta de Andaluacía.

April 2023: Tutorial: An introduction to Causal Inference for ML practitioners. Openbank, Madrid.

December 2022: Keynote Speaker at the 2022 AI4ES Summit. Topic: Causal Artificial Intelligence. Video (in Spanish).

November 2022:

Curso de Big Data e Inteligencia Artificial para gestores de salud, 18 de Noviembre, 2022, Santiago de Compostela. Salón de Actos de la Consellería de Sanidade. Organizado por Bioinformatics BCN.
Curso semipresencial sobre las nuevas tecnologías bioinformáticas y de análisis de datos masivos en el ámbito biomédico: Big Data & Inteligencia Artificial para gestores de la Salud”. Madrid. Hospital Universitario Ramón y Cajal. 11 de noviembre, 2022. Organizado por Bioinformatics BCN.

October 2022: Jornadas formativas sobre las nuevas tecnologías bioinformáticas y de análisis de datos masivos en el ámbito biomédico. Las Palmas de Gran Canaria Hospital Doctor Negrín. 17 de octubre, 2022. Organizado por Bioinformatics BCN.

September 2022: Invited Talk: Causality without Estimands: Application to Black-Box Explainability. September 26, 2022, Umea University, Sweden.

July 2022 - Accepted papers-

García, C., Mora, O., Pérez-Aragüés, F. et al. CatLC: Catalonia Multiresolution Land Cover Dataset. Sci Data 9, 554 (2022). https://doi.org/10.1038/s41597-022-01674-y
Á. Parafita and J. Vitrià, Estimand-Agnostic Causal Query Estimation with Deep Causal Graphs, in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3188395.

May 2022 - Accepted papers-

Brando, Axel, et al. Deep Non-Crossing Quantiles through the Partial Derivative. International Conference on Artificial Intelligence and Statistics. PMLR, 2022.
Pascual, Guillem, et al. Time-based Self-supervised Learning for Wireless Capsule Endoscopy. Computers in Biology and Medicine, Volume 146, 2022.

July 2021 - We are part of one of the 38 winning projects of the Artificial Intelligence in Health & Care NHS Award: Detecting bowel cancer – using AI to analyse video recordings of the gastrointestinal tract, taken from a swallowable camera, to target bowel cancer and other gastrointestinal diseases.

September 2020 - An app developed by @ICGCat that combines satellite data, big data, and artificial intelligence in order to monitor the use of water resources has won the top prize at this year’s European Space Agency @esa Space App Camp. This work is part of a successful industrial doctorate collaboration between @ICGCat and @datascienceUB @UniBarcelona @MatesInfoUB about the use of advanced deep learning methods for analyzing satellite data.

In the news:

July 2019 - Master in Foundations of Data Science

Feb. 2019 - Our project on artificial intelligence enables the improvement of colorectal cancer screening.

[DataScience] Endoscopy from Giny Comunicació on Vimeo.

Contact

Departament de Matemàtiques i Informàtica
Facultat de Matemàtiques i Informàtica
Universitat de Barcelona
Gran Via 585, 08007 Barcelona
email: jordi.vitria at ub.edu
tweeter: @bitenmascarado