Dr Thomas Ploetz
Lecturer (Context Aware Computing)

I am a Lecturer in "Context Aware Computing". My research agenda is centred on "Computational Behaviour Analysis", which basically means that I am building computational (that is statistical) models that describe and will help in assessing human behaviour. The basis for this is the analysis of behavioural data that is captured in a pretty opportunistic way utilising a variety of sensing modalities, most notably pervasive/ubiquitous sensors (e.g., accelerometers, RFID, environmental sensors), cameras, or microphones. The modelling itself is agnostic in terms of the actual choice of sensing modalities as long as the relevant information for behaviour analysis is captured. Behaviour data are sequential by definition. Consequently, related modelling techniques are focused on sequential models (for example of Markovian type). I am especially interested in quantitative assessments of human behaviour, which is of value for, for example, skill assessment. My day-to-day work can probably best be summarised as applied machine learning for activity recognition. 

The central theme of my research is to develop techniques and systems that actually have an impact on people's life. Therefore, my research is almost always connected to some practical application (in contrast to purely theoretical work) and I am keen on deploying systems I develop in the "wild", i.e., in real-world settings. The most prominent domain for this kind of work is health, where I am working on computational assessments of behavioural phenotypes of, for example, Parkinson's, Dementia, or Autism. Within the Digital Interaction group I am involved in a number of projects that address these research themes from different angles. 

A complete and up-to-date list of all my publications is maintained at my mendeley page.


I am a Lecturer in "Context Aware Computing". My research agenda is centred on "Computational Behaviour Analysis", which basically means that I am building computational (that is statistical) models that describe and will help in assessing human behaviour. The basis for this is the analysis of behavioural data that is captured in a pretty opportunistic way utilising a variety of sensing modalities, most notably pervasive/ubiquitous sensors (e.g., accelerometers, RFID, environmental sensors), cameras, or microphones. The modelling itself is agnostic in terms of the actual choice of sensing modalities as long as the relevant information for behaviour analysis is captured. Behaviour data are sequential by definition. Consequently, related modelling techniques are focused on sequential models (for example of Markovian type). I am especially interested in quantitative assessments of human behaviour, which is of value for, for example, skill assessment. My day-to-day work can probably best be summarised as applied machine learning for activity recognition. 

The central theme of my research is to develop techniques and systems that actually have an impact on people's life. Therefore, my research is almost always connected to some practical application (in contrast to purely theoretical work) and I am keen on deploying systems I develop in the "wild", i.e., in real-world settings. The most prominent domain for this kind of work is health, where I am working on computational assessments of behavioural phenotypes of, for example, Parkinson's, Dementia, or Autism. Within the Digital Interaction group I am involved in a number of projects that address these research themes from different angles. 

A complete and up-to-date list of all my publications is maintained at my mendeley page.