ICREA Academia

Petia Ivanova Radeva

ICREA Academia 2014

Universitat de Barcelona · Engineering Sciences

Petia Radeva completed her undergraduate study on Applied Mathematics at the University of Sofia, Bulgaria, in 1989. In 1996, she received a PhD degree in Computer Vision at UAB. In 2007, she moved as Tenured Associate professor at the Universitat de Barcelona (UB), Department of Mathematics and Informatics, where from 2009 to 2013 she was Director of Computer Science Undergraduate Studies. Petia Radeva is Head of the Consolidated Group Computer Vision at the University of Barcelona (CVUB) at UB and Head of the Medical Imaging Laboratory of Computer Vision Center (www.cvc.uab.es). She is a coautor of 24 international patents in the field of Computer Vision applied to Medical Imaging. Associate editor of International Journal of Visual Communication and Image Representation. She was a vice-chair of REA-FET-OPEN-2-2015 and REA-FET-OPEN-1-2016. She obtained an ICREA Academia in 2014 and the Prize “Antonio Caparrós” for the best technology transfer project of 2013.


Research interests

Petia Radeva’s research interests are on Development of learning-based approaches (specially, deep learning) for computer vision, and their application to health. Currently, she is involved on projects that study the application of wearable cameras and life-logging, to extract visual diary of individuals to be used for memory reinforcement of patients with mental diseases (e.g. Mild cognitive impairment). Moreover, she is exploring how to extract semantically meaningful events that characterise lifestyle and healthy habits of people from egocentric data. Other projects she is involved are: Machine learning tools for large scale object recognition, Food analysis by Computer Vision, Evaluation of intestinal motility by wireless endoscopy, Tissue characterisation and plaque analysis in carotid images, Automatic stent detection in IVUS, etc. She has h-index of 33 (Google Academic), with 1138 citations publishing 95 JCR articles and 232 international scientific publications.


Keywords

Computer Vision, Machine Learning, Medical Imaging, Health applications