Albert Bifet is Full Professor at University of Waikato and LTCI, Telecom Paris, IP-Paris. Previously he worked at Huawei Noah's Ark Lab in Hong Kong, Yahoo Labs in Barcelona, University of Waikato and UPC BarcelonaTech. He is the co-author of a book on Machine Learning for Data Streams at MIT Press, and author of a book on Adaptive Stream Mining and Pattern Learning and Mining from Evolving Data Streams. He is one of the leaders of MOA, scikit-multiflow, Apache SAMOA and streamDM software environments for implementing algorithms and running experiments for online learning from evolving data streams. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of KDD BigMine (2019-2012), and ACM SAC Data Streams Track (2020-2012)
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Artificial Intelligence/Machine Learning for Data Streams
Machine Learning of big data streams from sensors and devices is bound to become a key area of artificial intelligence research as the number of applications requiring such processing increases....
Massive Online Analytics for the Internet of Things (IoT)
Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. The challenge of deriving insights from the Internet of Things (IoT)...
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