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Dr Alexia Zoumpoulaki

Dr Alexia Zoumpoulaki


School of Computer Science and Informatics

Available for postgraduate supervision


In the past I have worked in various interdisciplinary research projects, including memory classification, deception detection and crowd simulations. Most of my work has focused in developing methods for analysing and classifying time series data.

I am working on multimodal processing of videos. Facial (micro) expressions - emotion recognition, text analysis, voice analysis in the area of deception detection using machine learning. I am interested in incorporating features extracted from these modalities with other psychological factors as well as features from eyetracking.

Broadly I am interested in congitive (computational) neursocience and how computer science can help model behaviour and cognitive processes.


  • PhD in Computer Science (Computational Neuroscience) - University of Kent, Canterbury, UK
  • MSc in Advanced Computer Science (Computational Intelligence): Distinction - University of Kent, Canterbury, UK
  • MSc Product & Systems Design Engineering: Distinction - University of the Aegean, Syros, Greece
  • BEng Information & Communication Systems Engineering - University of the Aegean, Samos, Greece










Computational Thinking CM6114

Undergraduate and Postrgraduate project supervision 

Research interests

I am interested in human factors, especially attention, multitasking and decision making. As a broader interest, I am interested in group behaviour especially under stressful situations.

I am using physiological measurements such as gaze, pupil dilation, eeg in combination with video, text and voice analysis to study the above areas. 

In the past I have mainly worked on neuroimaging projects, specialising on time series analysis.


I am currently looking to support PhD applications in the area of automatic deception detection. 

I am also interested in supervising PhD students in the areas of:

  • Video analysis - automatic micro expressions, gestures detection
  • Text analysis - deception detection using machine learning/neural networks
  • Voice analysis - features for deception detection
  • Eyetrackers - feature extraction for classification
  • HCI - visual attention, multitasking and problem solving - using eyetracking and/or neuroimaging