Dr Thomas Ploetz
Senior Lecturer

I am a Senior Lecturer in "Context Aware Computing".


My research agenda is centred on Applied Machine Learning for Computational Behaviour Analysis (CBA), that is I am interested in building computational systems for understanding behaviour through analysing activities. Behavioural data is thereby captured in an opportunistic way and utilising a variety of sensing modalities, most notably ubiquitous and wearable sensors but also cameras, or microphones. This agenda is driven by two main research questions:

  1. How can we design machine learning methods that are robust and reliable for real-world applications with specific focus on sensor data analysis?
  2. How can we build methods and systems that enable objective and accurate assessments of behaviour in naturalistic settings and thus directly contribute to improved understanding of behaviour with specific focus on health and wellbeing?

The key to these is fundamental research in ubiquitous and wearable computing, and specifically in innovative machine learning techniques with strong focus on their application related challenges. As an applied computer scientist I aim for the development and real-world deployment of innovative data analysis techniques beyond artificial settings, which typically comes with an additional and very different set of challenges beyond core method development. 

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.

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

I am a Senior Lecturer in "Context Aware Computing".

My research agenda is centred on Applied Machine Learning for Computational Behaviour Analysis (CBA), that is I am interested in building computational systems for understanding behaviour through analysing activities. Behavioural data is thereby captured in an opportunistic way and utilising a variety of sensing modalities, most notably ubiquitous and wearable sensors but also cameras, or microphones. This agenda is driven by two main research questions:

  1. How can we design machine learning methods that are robust and reliable for real-world applications with specific focus on sensor data analysis?
  2. How can we build methods and systems that enable objective and accurate assessments of behaviour in naturalistic settings and thus directly contribute to improved understanding of behaviour with specific focus on health and wellbeing?

The key to these is fundamental research in ubiquitous and wearable computing, and specifically in innovative machine learning techniques with strong focus on their application related challenges. As an applied computer scientist I aim for the development and real-world deployment of innovative data analysis techniques beyond artificial settings, which typically comes with an additional and very different set of challenges beyond core method development. 

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.

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

2015-16:

  • Machine Learning (CSC8111)
  • Technologies for Digital Civics (CSC8604)
  • Research Methods for Computing Science (CSC8009)
  • Research Methods for E-Business and Information Systems (CSC8405)

2014-15:

  • Machine Learning (CSC8111)
  • Technologies for Digital Civics (CSC8604)
  • Research Methods for Computing Science (CSC8009)
  • Research Methods for E-Business and Information Systems (CSC8405)

2013-14:

  • Programming and Data structures (CSC8001)
  • Research Methods for Computing Science (CSC8009)
  • Research Methods for E-Business and Information Systems (CSC8405)

2012-13: