Laboratory directions
Artificial Intelligence and Data Science Laboratory
Evaluate data quality, statistical models and practical uses of artificial intelligence.
Research scope
An applied-science perspective
To study how artificial intelligence and data methods help to understand complex systems, focusing on data quality, statistical modelling, machine learning and application evaluation. Laboratory attention is paid to the process of modelling judgement, as well as to the data, conditions of application and uncertainty on which the judgement is based.
Evaluate data quality, statistical models and practical uses of artificial intelligence.
Research directions
Statistical modelling and machine learning
Evaluation, uncertainty and interpretation
Working with evidence
Frame the question
Specify the system or situation, the question to investigate and the criteria used to judge an answer. Separate observations, assumptions and intended applications.
Examine the evidence
Identify data and document sources, access conditions and quality limits. Record analytical choices so that another participant can understand and review the process.
Compare and interpret
Compare methods or scenarios against stated criteria. Discuss uncertainty, limitations and alternative explanations rather than presenting a single result without context.
Formats for discussion
- A structured case study and review of sources
- A methodological note or documented model
- A workshop discussion around findings and limitations
Research exchange and participation
Discuss this laboratory
Introduce your research question, your background, the available materials and the form of exchange you propose. Use authorised, non-confidential examples for a first discussion.
Discuss this laboratory →