Sep 23–26, 2019

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

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

2:05pm–2:45pm Thursday, September 26, 2019

Location: 3B - Expo Hall

Machine learning for streaming data: Practical insights

Data Science, Machine Learning, & AI, Expo Hall

Secondary topics: Streaming and IoT, Telecom, Temporal data and time-series analytics

Heitor Murilo Gomes (Télécom ParisTech), Albert Bifet (Télécom ParisTech)

Heitor Murilo Gomes and Albert Bifet introduce you to a machine learning pipeline for streaming data using the streamDM framework. You'll also learn how to use streamDM for supervised and unsupervised learning tasks, see examples of online preprocessing methods, and discover how to expand the framework by adding new learning algorithms or preprocessing methods. Read more.