In this seminar, the research conducted over the past years will be presented, along with new projects or ideas to be implemented in the future. The objective is to share prior experiences and promote synergies with other researchers and groups within the Computer Vision Center.
Firstly, we will briefly discuss the research conducted during my predoctoral stage, which focused on privacy preservation in semi-structured data (i.e., graphs) and k-anonymity models. Next, we will explore how to approach the analysis and classification of patients with neurodegenerative diseases, such as multiple sclerosis (MS) or Alzheimer. In the initial approach, we will utilize graphs as a representation system for brain networks and employ graph theory metrics to extract relevant information from different brain regions and their impact on the progression of the disease. Alternatively, we can also employ 3D images as data sources to address the same problem. Finally, we will present some future ideas or interesting projects.
Jordi Casas-Roma holds a PhD in Computer Science (Universitat Autònoma de Barcelona, UAB, 2014), a Master's degree in Advanced Artificial Intelligence (Universidad Nacional de Educación a Distancia, UNED, 2011) and a Bachelor's degree on Computer Science (UAB, 2002). He was a senior lecturer at the Faculty of Computer Science, Multimedia and Telecommunications at Universitat Oberta de Catalunya (UOC, 2010-23) and also a part-time lecturer at Universitat Autònoma de Barcelona (2015-23). He was coordinator of the Master in Data Science at UOC. His teaching activities mainly concentrate on machine learning, deep learning, reinforcement learning and complex networks. His main research scopes include artificial intelligence, machine learning and graph mining. He was group leader at ADaS Lab (Applied Data Science Lab, 2020-23) where he investigated the application of artificial intelligence algorithms to real-world problems. He was a visiting researcher at the Data Science and Mining (DaSciM) group at the Computer Science Laboratory (LIX) of École Polytechnique (Paris, France, 2014).