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Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY
Albert Bifet

Albert Bifet
Professor and Head of Data, Intelligence, and Graphs (DIG) Group, Télécom ParisTech

Website | @abifet

Albert Bifet is a professor and head of the Data, Intelligence, and Graphs (DIG) Group at Télécom ParisTech and a scientific collaborator at École Polytechnique. A big data scientist with 10+ years of international experience in research, Albert has led new open source software projects for business analytics, data mining, and machine learning at Huawei, Yahoo, the University of Waikato, and UPC. At Yahoo Labs, he cofounded Apache scalable advanced massive online analysis (SAMOA), a distributed streaming machine learning framework that contains a programing abstraction for distributed streaming ML algorithms. At the WEKA Machine Learning Group, he co-led massive online analysis (MOA), the most popular open source framework for data stream mining with more than 20,000 downloads each year. Albert is the author of Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams and the editor of the Big Data Mining special issue of SIGKDD Explorations. He was cochair of the industrial track at ECML PKDD, BigMine, and the data streams track at ACM SAC. He holds a PhD from BarcelonaTech.

Sessions

3:30pm–4:10pm Thursday, 09/13/2018
Location: 1E 07/08 Level: Intermediate
Secondary topics:  Temporal data and time-series analytics
Heitor Murilo Gomes (Télécom ParisTech), Albert Bifet (Télécom ParisTech)
The StreamDM library provides the largest collection of data stream mining algorithms for Spark. Heitor Murilo Gomes and Albert Bifet explain how to use StreamDM and Structured Streaming to develop, apply, and evaluate learning models specially for nonstationary streams (i.e., those with concept drifts). Read more.