天美传媒

Joao Manuel Portela Gama - Universidades do Porto, Portugal

 is an Associate Professor at the University of Porto, Portugal. He is a senior researcher and member of the board of directors of the LIAAD, a group belonging to INESC Porto. He serves as member of the Editorial Board of MLJ, DAMI, TKDE, NGC, KAIS, and IDA. He served as Chair of ECMLPKDD 2005 and 2015, DS09, ADMA09 and a series of Workshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD.  His main research interest is in knowledge discovery from data streams and evolving data. He is the author of a recent book on Knowledge Discovery from Data Streams. He has extensive publications in the area of data stream learning.

Research stay at UC3M: DEPARTMENT OF SIGNAL THEORY AND COMMUNICATIONS

Project:

鈥―ata Mining is faced with new challenges. Large volumes of data are being generated from real time application domains such as mobility networks, social media, email networks, sensor networks, telecommunication networks etc. While such networks arise in a host of diverse arenas they often share important common properties. The data generated from these networks is massive, continuous and evolving from autonomous and geographically distributed sources, therefore regarded as network data streams. Our research goal consists of designing algorithms to analyse the evolving changes in these massive network structures. We are developing learning algorithms for clustering, predicting, and event detection from time evolving networked streams. Moreover, we are interested in developing meta-learning algorithms that self-monitor the evolution of the learning process, and have the ability of reasoning about it.