Professor  João Gama Digital Library

Based in Porto, Portugal
João Gama


João Gama is a Full Professor at the School of Economics, University of Porto, Portugal. He received his Ph.D. in Computer Science from the University of Porto in 2000. He is EurIA Fellow, IEEE Fellow, Fellow of the Asia-Pacific AI Association, and member of the board of directors of the LIAAD, a group belonging to INESC TEC. His h-index on Google Scholar is 67. He is an Editor of several top-level Machine Learning and Data Mining journals. He is ACM Distinguish Speaker. He served as Program Chair of ECMLPKDD 2005, DS09, ADMA09, EPIA 2017, DSAA 2017, served as Conference Chair of IDA 2011, ECMLPKDD 2015, DSAA’2021, and a series of Workshops on KDDS and Knowledge Discovery from Sensor Data with ACM SIGKDD, and ACM SIGAPP. His main research interests are in knowledge discovery from data streams, evolving data,  probabilistic reasoning, and causality. He published more than 300 reviewed papers in journals and major conferences. He has an extensive list of publications in data stream learning.


Available Lectures

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  • Current Trends in Learning from Data Streams
    Learning from data streams is a hot topic in machine learning and data mining. In this talk, we present three different problems and discuss streaming techniques to solve them. The first problem...
  • Data Mining for the XXI Century
    Nowadays, there are applications where data is best modeled not as persistent tables, but rather as transient data streams. In this talk, we discuss the limitations of current machine learning and...
  • Evolving Social Networks: trajectories of communities
    In recent years we witnessed an impressive advance in the social networks field, which became a ”hot” topic and a focus of considerable attention. The development of methods that focus...
  • Predictive Maintenance: the case of MetroPT
    Predictive Maintenance is part of a broader context. The goal is to identify the most probable causes and act to solve the problem before it escalates. In complex systems knowing that...

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