With over 20 years of experience and a global career in AI and data science, I focus on how machines learn, make decisions, and adapt to data streams that never stop. I serve as Professor of AI and Director of Te Ipu o te Mahara — The AI Institute at the University of Waikato, and Professor of Big Data at Télécom Paris, where I lead a joint lab with BNP Paribas.
I build research-to-impact pipelines: leading multidisciplinary teams, scaling international collaborations, and turning ideas into production systems. I helped create open-source foundations for stream learning—MOA (Massive Online Analysis), Apache SAMOA, and scikit-multiflow—and co-authored “Machine Learning for Data Streams” (MIT Press). Community service includes leadership roles (e.g., ECML-PKDD 2024 General Co-Chair) and recognition, such as a Best Paper Award at ACM FAccT 2023.
Industry & application focus: I’ve partnered with startups, telcos, financial services, and environmental organisations to apply adaptive, responsible AI at scale—designing solutions for concept drift, real-time monitoring, low-latency inference, and end-to-end MLOps. Colleagues call on me to untangle hard problems, align research with strategy, and structure collaborations that move from pilot to deployment.
Leadership & community: Regular keynote and invited speaker; contributor to governance and standards (including work aligned with ISO/IEC SC 42); active in the NZ AI Researchers Association (500+ members); and member on the AI Forum Executive Committee. I’m also a long-time supervisor of PhD students and postdocs, developing capability pipelines that strengthen both academia and industry. I champion open source as the engine of trustworthy, reproducible AI.
How can I help:
• Advise executives on AI strategy, governance, responsible AI and risk
• Build adaptive ML systems for streaming/sensor data and concept drift
• Co-design R&D partnerships, grants, and tech transfer from lab to product
• Mentor and supervise PhDs/postdocs; develop high-calibre teams
• Shape open-source strategy (licensing, community, sustainability)
• Deliver keynotes/workshops for technical and executive audiences
What’s new
- Keynote Speaker, Australasian Joint Conference on Artificial Intelligence 2026
- 2 papers accepted at AAAI-26 Main Track, 1 paper accepted at AAAI-26 Journal Track
- Elliker, C., Read, J., Vanier, S., Bifet, A. (2026). Simulation-Driven Railway Delay Prediction: An Imitation Learning Approach. AAAI Conference on Artificial Intelligence (AAAI).
- Yu, P., Chen, Y., Xu, C., Bifet, A., Read, J. (2026). Binary Split Categorical Feature with Mean Absolute Error Criteria in CART. AAAI Conference on Artificial Intelligence (AAAI).
- Marcos Vinicius Ferreira, Matheus Souza, Tatiane N. Rios, Islame F. C. Fernandes, Jorge Nery, João Gama, Albert Bifet, Ricardo Araújo Rios(2026). Salvador Urban Network Transportation (SUNT): A Landmark Spatiotemporal Dataset for Public Transportation. AAAI 2026: 39864
- Organizing AI for Environment Science Workshop at AAAI-26
- Keynote speaker at Streaming Continual Learning Bridge at AAAI-26
- 1 paper accepted at KDD-26
- Mungari, S., Bifet, A., Manco, G., Pfahringer, B. (2026). ARES: Anomaly Recognition Model for Edge Streams. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD