About TuiML
TuiML is the agent-native ML runtime: classical machine learning that AI agents can actually call. It is developed by a team of researchers and engineers at the AI Institute, University of Waikato. Our approach combines research insights with practical development efforts, ensuring the project maintains academic rigor while also being applicable in real-world scenarios.
Core maintainers
Active developers who oversee discussions, manage releases, ensure module integration, and handle coding tasks in general.
Nilesh is a Ph.D. candidate at the University of Waikato's AI Institute, working on the TAIAO project and AutoML for data streams. He contributes to Weka Web and Weka Copilot, and has built several open-source tools including Deep Image Search, AutoWave, and Deep Text Search. His work focuses on making machine learning more accessible through better tooling and interfaces.
Nick is a Postdoctoral Research Fellow at the AI Institute, University of Waikato, with a background in statistical learning. His research focuses on deep learning explainability and environmental data science, including weak supervision, uncertainty estimation, and applying machine learning to ecological datasets. Previously, he was a Design Automation Engineer at Intel.
Research advisors
Senior researchers who guide the project's scientific direction. They shape the research agenda and ensure our methods stay grounded in the state of the art.
Albert is Professor of AI and Director of the AI Institute at the University of Waikato, with over 20 years of experience in machine learning. He created MOA and Apache SAMOA, the leading open-source frameworks for stream learning, and co-authored "Machine Learning for Data Streams" (MIT Press, 2023).
Bernhard is a Professor and co-director of the AI Institute at the University of Waikato. He has been a core contributor to WEKA for many years and helped build MOA for stream classification. His research covers streaming data, ensemble methods, and multi-label classification.