Mr Demetris Lappas

Research project: Anomaly Detection in Computer Vision using Deep Learning Models


Anomaly detection is the practice of detecting rare patterns in data, which deviate beyond a particular distribution of expected behaviour. Anomalies by definition are rare, Automated mechanisms that are able to detect anomalies would be valuable as they have the potential to significantly reduce human operators time and therefore minimise errors and costs.

Classic machine learning approaches tackle the problem by measuring the distance between data points with respect to the area occupied by the majority of normal data. However, this is more challenging within computer vision and more sophisticated models are required to detect anomalies. My research aims to develop new models for recognising arbitrary anomalies within images and/or videos using combinations of various machine learning and deep learning techniques.


I am currently a full-time profesional Senior Data Scientist operating in the financial sector, where I develop Deep Learning models for Entity Resolution. I have worked for financial giants such as Barclays Bank and Ernst & Young within the departments of Cards and Payments, Financial Crime, and Entity Resolution Data Enrichment.My original background is in pure mathematics and later branched off into Data Analytics, Data Science and Artificial Intelligence. I am partaking in a part-time PhD titled Anomaly Detection in Computer Vision using Deep Learning Models which will hopefully be the first step in much research to come.

Areas of research interest

  • Artificial Intelligence
  • Computer Vision
  • Anomaly Detection
  • Machine Learning
  • Deep Learning


  • BSc in Mathematics, London Metropolitan University
  • Postgraduate in Mathematics, University of Glasgow