Talks and presentations

SSL and Delayed Labelling

May 18, 2023

Presentation, University of Waikato, Online

Guest Lecture concerning SSL and Delayed Labelling for undergraduate students at the University of Waikato (Hamilton, NZ)

Machine Learning for Streaming Data

April 25, 2023

Presentation, China University of Mining and Technology, Online

Guest Lecture concerning Machine Learning for Streaming Data for graduate students at China University of Mining and Technology

Concept Drift Detection and Applications

March 31, 2023

Presentation, Stats NZ data science seminar, Online

Guest Lecture concerning Concept Drift Detection and Applications for data scientists from Stats NZ

Machine Learning for Streaming Data

September 22, 2022

Presentation, Michigan Technological University, Online

Guest Lecture entitled “Machine Learning for Streaming Data” for undergrad and graduate students at Michigan Technological University (USA)

Learning for Delayed Partially Labelled Data Streams

December 07, 2021

Presentation, ICDM workshop (IncrLearn 2021), Auckland, New Zealand

Keynote at the IncrLearn workshop in ICDM 2021. Program: Slides:

Machine Learning for Streaming Data

May 17, 2021

Presentation, Cardiff University seminar, Online

Invited talk at the Joint Machine Learning Seminar hosted collaboratively by Cardiff University and the University of Waikato

Machine learning for data streams with scikit-multiflow

January 07, 2021

Tutorial, International Joint Conference on Artificial Intelligence, Yokohama, Japan

In this tutorial we introduced attendees to data stream mining procedures and examples of big data stream mining applications with examples using the scikit-multiflow. The tutorial was scheduled for IJCAI 2020 but ended up being presented early 2021 due to the pandemic. Website:

Lecture at the IOT Stream Data Mining course

November 20, 2019

Lecture, Telecom Paris, Paris, France

Lecture at the IOT Stream Data Mining course (Paris, France) as part of the Data and Knowledge 2nd year Master Program of Université Paris Saclay 2019-2020.

Machine learning for streaming data: Practical insights

September 26, 2019

Talk, Javits Center, New York, USA

In many domains, data is generated at a fast pace. A clear example is the Internet of Things (IoT) applications, where connected sensors yield large amount of data in short periods. To build predictive models from this data, you need to either settle for traditional offline learning or attempt to learn from the data incrementally. A significant setback with the offline learning approach is that it’s slow to react to changes in the domain, and these changes can have a catastrophic impact on the model predictive performance, since the patterns in which the model was trained on are no longer valid. An online approach where the model is trained incrementally can potentially fix this; however, the untold story is that the existing challenges for offline learning are still present (and are even maximized) when processing the data online. These challenges include, but are not limited to, raw data preprocessing, efficient incremental updates to models, algorithms to detect changes and react to them, and dealing with lots of unlabeled and delayed-labeled data.

Streaming Random Forest Learning in Spark and StreamDM

October 03, 2018

Talk, ExCel London, London, UK

We present how to build random forest models from streaming data. This is achieved by training, predicting and adapting the model in real-time with evolving data streams. The implementation is on the open source library StreamDM, built on top of Apache Spark.

Machine learning for nonstationary streaming data using Structured Streaming and StreamDM

September 13, 2018

Talk, Javits Center, New York, USA

The main difference between batch machine learning implementations in Spark (MLlib and Spark ML) and StreamDM is that the latter focus on algorithms that can be trained and adapted incrementally. This can be a huge advantage in some domains as it enables automatically updating the learning models. StreamDM is currently under development by Huawei Noah’s Ark Lab and Télécom ParisTech.

Big Data Stream Mining using Spark Streaming

September 10, 2018

Tutorial, Croke Park Conference Centre, Dublin, Ireland

The volume of data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. In addition, these models must take into account many constraints: (pseudo) real-time processing, high-velocity, and dynamic multi-form change such as concept drift and novelty. The tutorial was combined with a workshop on the same topic.

Machine Learning for Spark Streaming with StreamDM

July 08, 2018

Tutorial, Barra da Tijuca Beach Windsor, Rio de Janeiro, Brazil

The main goal of this tutorial is to introduce attendees to big data stream mining theory and practice. We will use the StreamDM framework to illustrate concepts and also to demonstrate how data stream mining pipelines can be deployed using StreamDM.

StreamDM: Advanced data science with Spark Streaming

May 23, 2018

Tutorial, ExCel London, London, UK

An overview of StreamDM, a real-time analytics open source software library built on top of Spark Streaming, developed at Huawei’s Noah’s Ark Lab and Télécom ParisTech.

Ensemble Learning for Evolving Data Streams

July 01, 2017

Talk, Harbin Institute of Technology, Shenzhen, China

Invited talk at the Harbin Institute of Technology (HIT) (Shenzhen, China): Ensemble Learning for Evolving Data Streams.