Rigour
We define the problem carefully, document the data, and evaluate claims against appropriate evidence.
About MLKD
A focused home for learning systems and knowledge discovery.
We connect methodological depth with questions from science, public service, the environment, and society.
Our purpose
From models to evidence people can use
MLKD is a faculty group that advances machine learning, data mining, natural language processing, computer vision, and responsible decision support. The group provides a shared direction for research, teaching, student supervision, and external collaboration.
We value careful problem formulation, transparent evaluation, reproducible work, and clear communication. A strong model matters, but so do the data, the assumptions, and the decisions that follow.
How we work
Shared standards, practical outcomes
Relevance
We connect technical choices with real needs and clear operational or scientific value.
Responsibility
We consider explainability, limitations, human oversight, and the consequences of deployment.
Collaboration
We build research across faculty, students, disciplines, government, and community partners.
Research direction
Roadmap 2026–2030
Foundation
Map expertise, strengthen datasets, align student work, and establish shared research practices.
Integration
Connect predictive models, knowledge discovery, and decision-support applications.
Validation
Expand field validation and interdisciplinary collaboration around selected themes.
Translation
Develop reusable platforms, research assets, and stronger pathways to adoption.
Leadership
Consolidate MLKD as a visible centre of applied, responsible machine learning research.