Albert Bifet
Co-chair NZ AI Researchers Association
New Zealand
Summary
👤
Senior🎓
Top SchoolAlbert Bifet is a veteran AI researcher and leader with over two decades of experience building adaptive machine learning systems for real-time, streaming data and translating them into production impact across academia and industry. As Professor of AI and Founding Director of Te Ipu o te Mahara at the University of Waikato and Professor of Big Data at Télécom Paris, he has steered large multidisciplinary programmes and created widely used open-source foundations for stream learning (MOA, SAMOA, scikit-multiflow). He combines deep technical authorship—including the MIT Press book Machine Learning for Data Streams and core contributions like Hoeffding tree implementations—with practical deployments for telcos, finance, and environmental monitoring. A community builder and policy influencer, he co-chairs the NZ AI Researchers Association, helped shape national AI strategy whitepapers, and served in key conference and standards roles. Colleagues rely on him to untangle concept drift, low-latency inference, and end-to-end MLOps challenges while mentoring the next generation of researchers.
16 years of coding experience
3 years of employment as a software developer
UPC Universitat Politècnica de CatalunyaHDR Computer Science, HDR Computer Science at Pierre and Marie Curie University
Catalan, Spanish, English, maori, French
Stackoverflow
31reputation
3kreached
2answers
0questions
Github Skills (15)
algorithm10
data-stream10
javas10
machine-learning10
machine-learning-algorithms10
data-streaming10
spark-streaming10
java10
data-streams10
scala10
clustering9
apache-spark9
moa6
weka6
classification6
Programming languages (5)
Github contributions (5)
Waikato/moa
Jul 2009 - Apr 2022
MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:3 releases, 1 review, 351 commits in 12 years 11 months
Contributions summary:Albert's initial import of the `HoeffdingOptionTree.java` suggests they are likely responsible for contributing to and maintaining the core machine learning algorithms within the project, specifically focusing on decision tree implementations. Further commits reveal integration efforts of MOA classifiers and data generators into the WEKA environment, implying a role in linking the project's machine learning components with external libraries and tools. Subsequent modifications to the `WEKAClassifier` indicate involvement in the adaptation and integration of existing classifiers with this integration.
pythondata-streamstreamdriftclassification
huawei-noah/streamDM
Jan 2015 - Jul 2015
Stream Data Mining Library for Spark Streaming
Role in this project:Back-end Developer
Back-end DeveloperContributions:87 commits, 4 pushes, 4 comments in 5 months
Contributions summary:Albert primarily focused on modifying and extending the `streamdm` library for Spark Streaming. Their contributions involved updating the folder structure of the project and adding features such as parameters and Javadoc comments. Furthermore, the user added and modified the core classes that related to machine learning and data streaming.
miningdata-streamdata-miningstreamsstreaming
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.