Álvaro García-Piquer Homepage
N�ria Maci�


In 2007 I completed my degree in computer engineering and in 2012 I obtained my PhD in Information Technology and Management at La SalleRamon Llull University. Currently I am a researcher at Ramon Llull University and I am member of the Research Group in Intelligent Systems (GRSI) of the same university.

GRSI is a research group founded in 1994, and recognized by the Government of Catalonia since year 2002 (2002-SGR-00155, 2005-SGR-00302, 2009 SGR-183). The group focuses its activity on Data Mining and Machine Learning under the approaches of Soft-Computing techniques as Soft-Case-Based Reasoning, Evolutionary Computation, Neural Networks and Fuzzy Logic.

My PhD thesis under the Information Technology and Management program was guided by Dr. Elisabet Golobardes and Dr. Albert Fornells. It was focused on applying data mining for pattern recognition in several domains, as education, network nets, medicine or energy sector. Concretely, the main topic was based on evolutionary multiobjective clustering techniques. In 2010 I obtained a grant for researchers formation (2010FI_B 01084, 2011FI_B1 00022) by the Government of Catalonia for carry out my PhD, and in 2011 I obtained a fellowship for international research internships (2010BE 01026) by the Government of Catalonia. From September 2010 to April 2011 I visited The Automated Scheduling, Optimisation and Planning research group (ASAP) from the University of Nottingham (UK).

I am also a researcher in R+D projects, at this time I am working on the Spanish Government funded project KEEL III (TIN2008-06681-C06-05) which aims at conducting research on some of the hot topics in data mining. I also worked on two other funded projects: GAD and MID-CBR. The aim of GAD (CEN200710126) was to acquire knowledge about electric grid optimization objectives such as peak reduction (demand shifting), energy efficiency, demand response and environmental impact reduction. The goal of MID-CBR (TIN2006-15140-C03-03) was to define an integrative framework for the development of Case-Based Reasoning Systems.