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I am a Professor at Data, Intelligence and Graphs (DIG) LTCI, Télécom Paris and University of Waikato. My research focuses on Artificial Intelligence, Big Data Science, and Machine Learning for Data Streams.

I am co-author of the book “Machine Learning for Data Streams” of MIT Press and I’m co-leading the open source projects MOA Massive On-line Analysis, scikit-multiflow, StreamDM for Spark Streaming and Apache SAMOA Scalable Advanced Massive Online Analysis.

I am an ACM Distinguished Speaker on Artificial Intelligence, Big Data, and Computer Science. See my last Keynote Talks.

What’s new

Machine Learning for Data Streams: with Practical Examples in MOA

  • Series: Adaptive Computation and Machine Learning series
  • Hardcover: 288 pages
  • Publisher: The MIT Press (March 2, 2018)
  • Language: English
  • ISBN-10: 0262037793
  • ISBN-13: 978-0262037792

Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.

Invited Talks

Invited Talks