Skip to content

Dr Matthias Treder

Lecturer

School of Computer Science and Informatics

Email:
trederm@cardiff.ac.uk
Telephone:
+44 (0)29 2251 0960
Location:
WX/3.12, Queen's Buildings - West Building Extension, 5 The Parade, Newport Road, Cardiff, CF24 3AA

My research interests revolve around machine learning, multivariate time-series analysis, and medical applications. I have been working in machine learning and its application in neuroscience and brain-computer interfaces since 2009.

I developed the open-source MVPA Light Toolbox for classification of neuroimaging time series data.

Education and qualifications

  • 2017: BSc Mathematics, TU Berlin, Germany
  • 2010: PhD in Cognitive Psychology
  • 2005: Propedeuse diploma in Computer Science, Radboud University, The Netherlands
  • 2004: MSc in Cognitive Psychology, Radboud University, The Netherlands

Honours and awards

  • Travel Award, Guarantors of Brain Registered charity (2015)
  • Brain-Computer Interfacing HCI 2011 Design Challenge, 1st prize (2011)

Academic positions

  • 2018 - present: Lecturer, Cardiff University, UK
  • 2017 - 2018: Research Fellow, Cardiff University, UK
  • 2016 - 2017: Research Fellow, University of Birmingham, UK
  • 2015 - 2016: DAAD (Germany) teaching fellow
  • 2014 - 2015: Research Fellow, University of Cambridge, UK
  • 2009 - 2014: Research Fellow, Machine Learning Lab, TU Berlin, Germany

Committees and reviewing

  • Journal reviewer: IEEE TBME, J Neural Eng, NeuroImage

2019

  • Treder, M. 2019. Direct calculation of out-of-sample predictions in multi-class kernel FDA. Presented at: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 24-26 April 2019ESANN 2019 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN pp. 245-250.

2018

2017

2016

2015

2014

My teaching includes Python programming, statistical data analysis and visualisation.

Research interests include

  • Interpretability of statistical models
  • Kernel methods
  • Optimisation in supervised learning
  • Statistical learning in large datasets (many instances and/or many dimensions)
  • Development of machine learning toolboxes

I am interested in supervising PhD students in the area of machine learning and data analysis, both fundamental and applied  research. Specific topics include

  • supervised learning
  • optimisation
  • time-series analysis
  • applications in brain-computer interfaces
  • applications in neuroimaging and psychiatry