FORMACIÓN ACADÉMICA

• Doctor Ingeniero en Informática por la Escuela Politécnica Superior de la Universidad Carlos III de Madrid (2003).
• Ingeniero en Informática por la Escuela Politécnica Superior de la Universidad Carlos III de Madrid (1999).
• Ingeniero Técnico en Informática por la Escuela Superior de Informática de la Universidad Complutense de Madrid (1996).

ACTIVIDADES ANTERIORES DE CARÁCTER CIENTÍFICO

• Postdoctoral Fellow. Carnegie Mellon University 2004-2005
• Ayudante Doctor. Universidad Carlos III de Madrid 2003-2004
• Ayudante de Escuela Univeristaria. Universidad Carlos III de Madrid 2001-2003
• Becario F.P.U. Universidad Carlos III de Madrid 2000-2001
• Visitor Scholar. Oak Ridge National Laborartory. Estados Unidos 2001
• Becario Colaboración proyecto europeo SHAMASH. Universidad Carlos III de Madrid 1998-1999

 PUBLICACIONES

Visita la página de publicaciones de Fernando Fernández Rebollo para obtener su lista completa de publicaciones.

 Fernando Fernández is a faculty of the Computer Science Department of Universidad Carlos III de Madrid, since october 2005. He received his Ph.D. degree in Computer Science from University Carlos III of Madrid (UC3M) in 2003. He received his B.Sc. in 1999 from UC3M, also in Computer Science. Since 2001, he became assistant and associate professor at UC3M. In the fall of 2000, Fernando was a visiting student at the Center for Engineering Science Advanced Research at Oak Ridge National Laboratory (Tennessee). He was also a postdoctoral fellow at the Computer Science Department of Carnegie Mellon University (visit his old web page at CMU) since october 2004 until december 2005. He is the recipient of a pre-doctoral FPU fellowship award from Spanish Ministry of Education (MEC), a Doctoral Prize from UC3M, and a MEC-Fulbright postdoctoral Fellowship. He has more than 30 jounal and conference papers, mainly in the field of machine learning and planning (see a complete list of publications here). He is interested in intelligent systems that operate in continuous and stochastic domains. In his PhD thesis, he studied different discretization methods of the state space in Reinforcement Learning problems. When he arrived to CMU, he focused his research on the transfer of policies between different Reinforcement Learning tasks. Currently, his research is also focused on connecting classical planning methods with learning mechanism that allow to behave in dynamic and stochastic environments. Applications of his research include robot soccer, adaptive educational systems, tourism support tools and business intelligence.

• Inteligencia Artificial, Ingeniería Informática
• Aprendizaje Automático, Ingeniería Informática
• Inteligencia Artificial, Ingeniería Técnica en Informática de Gestión
• Programación Automática, Máster en Ciencia y Tecnología Informática 

Información para alumnos: visita Aula Global