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Thesis defence: Sergio Sánchez Martínez

Thesis defence: Sergio Sánchez Martínez

02.10.2018
Multi-feature machine learning analysis for an improved characterization of the cardiac mechanics
By
Sergio Sánchez Martínez
 
Thesis supervisors: Dr. Gemma Piella, Dr. Bart Bijnens & Dr. Nicolas Duchateau (Univ. Lyon I).
21/09/2018
 
Thesis Brief Description:
 
My thesis focuses on the development of machine learning tools to better characterize the cardiac anatomy and function in the context of heart failure, and in particular their extension to consider multiple parameters that help to identify the pathophysiological aspects underlying disease. This advanced and personalized characterization may eventually allow assigning patients to clinically-meaningful phenogroups with a uniform treatment response and/or disease prognosis. Specifically, the thesis copes with the technical difficulties that multivariate analyses imply, paying special attention to properly combine different descriptors that might be of different nature (e.g., patterns, continuous, or categorical variables) and to reduce the complexity of large amounts of data up to a meaningful representation. To this end, we implemented an unsupervised dimensionality reduction technique (Multiple Kernel Learning), which highlights the main characteristics of complex, high-dimensional data into fewer dimensions. For our computational analysis to be useful for the clinical community, it should remain fully interpretable. We made special emphasis in allowing the user to be aware of how the input to the learning process models the obtained output, through the use of multi-scale kernel regression techniques among others.
 
Experience as a PhD Student:
 
These 4 years at UPF have been very exciting, both from the academic and personal perspectives. I've had the opportunity to meet and interact with experienced researchers, who taught me to have a critical thinking and to be creative. I've participated in European projects, where I've worked in large teams of experts in my field of research. I've attended several technical and clinical conferences, where I had the opportunity to know the cutting-edge advancements in machine learning for clinical applications. Thanks to my scholarship and my supervisor's network, I've done two research stays, one in France and the other in the US, where I had the opportunity to collaborate with other teams, always learning new approaches to research. Finally, I'm very grateful for having met all my PhD fellows, who have created a fantastic working atmosphere where I truly felt at home. Among them, I can count some of my best friends. 

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