Profile

My field of research aims to broaden the understanding of cardiac function and cardiovascular diagnostic techniques through the application of fluid mechanics and mathematical modeling tools. This philosophy has allowed us to delve into previously unknown aspects of cardiac physiology and propose new diagnostic tools for cardiovascular disease. You can download my resume/CV here.

Research Experience

Assistant Professor

2021 - Present
UNED, Madrid

Adjunct Professor

2019 - 2021
Universidad Carlos III, Madrid

Postdoctoral Researcher

2016 - 2021
CIBERCV, Madrid

Postdoctoral Researcher

2014 - 2016
Juan de la Cierva Fellowship, Hospital Gregorio Marañòn, Madrid

Postdoctoral Researcher

2012 - 2014
University of California San Diego

Teaching @ UNED

At the moment I teach the following undergraduate courses .

61044069 - Batchelor in Physics: TÉCNICAS EXPERIMENTALES IV.
61043101 - Batchelor in Physics: TÉCNICAS EXPERIMENTALES III.
61044081 - Batchelor in Physics: FÍSICA MATEMÁTICA.
61044052 - Batchelor in Physics: FÍSICA DE FLUIDOS.

Also I teach the following graduate courses .

21153189 - MsC in Medical Physics: FUNDAMENTOS FÍSICOS DE LA IMAGEN MÉDICA I.
21153193 - MsC in Medical Physics: FUNDAMENTOS FÍSICOS DE LA IMAGEN MÉDICA II.
21153121 - MsC in Medical Physics: FÍSICA DE FLUIDOS FISIOLÓGICOS.
21153136 - MsC in Medical Physics: FÍSICA MATEMÁTICA.
21153263 - MsC in Medical Physicsa: INSTRUMENTACIÓN BIOMÉDICA.
21153263 - MsC in Medical Physics: TRATAMIENTO DE SEÑALES.

Selected Grants (2/25)

Artificial Intelligence for Non-invasive Hemodynamics. AI4NHEM

2021 - 2024
PI20/00587. Instituto de Salud Carlos III, Madrid, Spain.

Echocardiography is the most used imaging technique in cardiovascular medicine. Despite extensive research during decades, echocardiography is limited to predict accurately critical hemodynamic variables such as pulmonary mean and capillary pressures and cardiac output. Thus, invasive right-heart catheterization is still routinely performed to obtain these measurements. We hypothesize that an appropriately designed and trained deep-learning algorithm will improve the accuracy of current hemodynamic estimations derived from ultrasound. The main objective of this proposal is to settle the bases for a deep neural network for providing clinicians with reliable noninvasive estimators of cardiac hemodynamic data.

Clinical Implications of intraventricular flows in patients with heart failure

2017 - 2020
DPI2016-75706-P, Ministerio de Economía y Competitividad, Madrid, Spain.

The overarching aim of this study was to implement, validate and transfer to the clinical practice new post-processing methods of cardiac-imaging flow data, in order to widen the characterization and understanding of cardiac physiology while targeting their clinical application. These methods are based on the measurement and quantification of the blood velocity fields inside healthy and diseased human left ventricles (LV), as well as in the indices derived from them.

Publications

You can find an updated list of my publications in Google Scholar or in Scopus

Awards

In 2015, I was awarded with the Parmley Prize by the American College of Cardiology along with my mentors Juan Carlos del Alamo and Javier Bermejo