Simone Pezzuto

+41 58 666 4976
Institute of Computational Science
Faculty of Informatics
Via Giuseppe Buffi 13
6900 Lugano

Simone Pezzuto

CCMC Group Leader

Simone Pezzuto is Group Leader at the Center for Computational Medicine in Cardiology (CCMC). The center of the research activities of Dr. Pezzuto lies at the interdisciplinary intersection between applied mathematics and cardiac physiology. In the spirit of the CCMC vision, Dr. Pezzuto fosters a tight collaboration with the clinical partners to translate mathematical modeling of the heart into clinical applications. While being a numerical analyst by education, Dr. Pezzuto collaborates on a daily basis with clinical cardiac electrophysiologists at addressing, by means of computer models, questions of clinical interest.

The group of Dr. Pezzuto, currently consisting of 1 postdoc (Dr. Ali Gharaviri) and 1 PhD student (Lia Gander), has the objective to develop novel numerical methods to tackle problems of clinical relevance, such as inverse problems and parameter identification, uncertainty quantification, and efficient simulation of both cardiac electrophysiology and mechanics.

Patient-Specific modeling

Patient-Specific Modeling
Patient-specific modeling in cardiac electrophysiology is based on sophisticated mathematical models which require special care in their numerical solution and parametrization. Given the increasing availability of patient data, from the standard 12-lead electrocardiogram to minimally invasive, high-density catheter mapping, we employ modern numerical algorithms to assimilate such data into the model for further individualization and, ultimately, improved therapeutic intervention. Amongst our recent achievements, we have been able to identify sites of earliest ventricular activation from the 12-lead ECG and to reconstruct the fiber architecture and conduction velocity from contact-mapping recordings.

Uncertainty Quantification

Fibrosis Random Field In view of clinical applications, and given its importance, our group has recently started to include uncertainty quantification (UQ) into our electrophysiology models. The strategy we pursued was to combine existing models for cardiac electrophysiology to accelerate the standard Monte Carlo procedure (multifidelity approach). Incidentally, we have also developed a method for sampling random fields on general and possibly complex geometries, such as the heart anatomy.


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