He has a B.S. degree in Telecom Engineering, from the Universitat Politècnica de Catalunya and a PhD from the Universitat de Girona. He worked as junior research in 1991 at Cognivision research and as electronic engineer between 1991 and 1992 at OPTIMUS SA. In 1992 started the academic career at the University of Girona where in 1999 he won a permanent position at the Department of Electrical, Electronic and Automation Engineering where he has developed his career until now. He has been responsible of several engineering degrees, master programs and coordinator of the PhD Program in Technology at this University from 2007 until 2015. He leads the research group “Control Engineering and Intelligent Systems, eXiT” (http://exit.udg.edu), at the Institut d’Informàtica i Aplicacions, focusing his research on data-driven methods for process supervision and decision support with emphasis in applications for power distribution networks and energy efficiency and is the director of the the UdG Chair on Smart Cities and co-funder (2011) of the Spin Off Newronia SL.
Research interest is on data driven methods for process supervision and decision support, including machine learning and statistical methods. In particular, multivariate statistical methods for fault detection and diagnosis have been developed and tested in different domains (power systems, power quality, aerospace, waste-water treatment plants, water and power smart grids, petrochemical, etc.); case based reasoning solutions have been proposed for decision support in medical and industrial applications. Sequential pattern discovery approaches are being studied for forecasting purposes in power systems (power quality assessment and fault prediction) and urban mobility. He has led several research projects and contracts at national and international level, and he is author of more than 120 papers in international conferences and journals in these areas.
Main interests on energy/water efficiency fall in the area of monitoring, at building, neighbourhood and community levels including smart grids. In particular modelling and profiling consumptions from historical data and exploiting those models for detecting abnormal behaviours and deviations. Application of such methods include modelling to support measure and verification procedures, automatic fault detection and diagnosis to support energy management operation at community and grid level, energy forecasting as well and optimisation of resources in scheduling problems.