Professor Sotirios Tsaftaris




+44(0)131 6505796


2.06 Alexander Graham Bell Building

Personal Page: 

Engineering Discipline: 

  • Electronics and Electrical Engineering

Research Institute: 

  • Imaging, Data and Communications

Research Theme: 

  • Signal and Image Processing
Prof. Sotirios Tsaftaris
Prof. Sotirios Tsaftaris


Sotirios A. Tsaftaris is currently Chair (Full Professor) in Machine Learning and Computer Vision at the University of Edinburgh. He also holds the Canon Medical/Royal Academy of Engineering Research Chair in Healthcare AI. He is an ELLIS Fellow of the European Lab for Learning and Intelligent Systems (ELLIS) of Edinburgh’s ELLIS Unit. Since 2023 he is a visiting researcher with Archimedes RC a research centre of excellence in AI in Athens, Greece. Between 2016 and 2023 he was a Turing Fellow with the Alan Turing Institute.

He received the M.Sc. and Ph.D degrees in Electrical and Computer Engineering from Northwestern University, Evanston, IL, in 2003 and 2006, respectively, and the Diploma degree in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Thessaloniki, Greece, in 2000.

Previously he was an Assistant Professor with IMT Institute for Advanced Studies, Lucca, Italy and the Director of the Pattern Recognition and Image Analysis Unit at IMT. Prior to that, he held a joint Research Assistant Professor appointment at Northwestern University with the Departments of Electrical Engineering and Computer Science (EECS) and Radiology Feinberg School of Medicine. He maintained an adjunct appointment with EECS (2011-2015), and an affiliation with the Image and Video Processing Laboratory (IVPL), at Northwestern University.

He has published extensively, particularly in interdisciplinary fields, with more than 180 journal and conference papers in his active record, with a variety of co-authors and collaborators.

While he has served in many technical program committees of international conferences, and he actively reviews papers for several prestigious international journals, most notably he currently is an Associate Editor (AE) for the IEEE Transactions on Medical Imaging. He served as an AE for IEEE Journal of Biomedical and Health Informatics (2011-2021) and Elsevier DSP (2014-2018). He was tutorial chair for ECCV 2020. He was Doctoral Symposium Chair for IEEE ICIP 2018 (Athens). He has served as area chair for CVPR 2021, MICCAI 2018 (Granada), ICME 2018 (San Diego), ICCV 2017 (Venice), MMSP 2016 (Montreal), VCIP 2015 (Singapore). He has also co-organized workshops and tutorials for ECCV (2020, 2014), CVPR (2019), ICCV (2017), BMVC (2015), and MICCAI (2016, 2017, 2021).

He is a member of the IEEE, Senior MemberISMRM, and SCMR.

His work has received several accolades, such as Best Paper Award (STACOM 2017), twice a Magna Cum Laude Award (ISMRM), a finalist for the Early Career Award (SCMR, 2011; SCMR, 2019 (Chartsias as PhD student)), and has had his work appear in journal covers and attract significant media coverage.

Prof. Tsaftaris is also a Murphy Fellow and a Fellow of the Alexander S. Onassis Public Benefit Foundation.

Academic Qualifications: 

  • 2000 - Diploma (5 year), Aristotle University of Thessaloniki (Greece), Electrical and Computer Engineering
  • 2003 - MSc, Northwestern University (USA), Electrical and Computer Engineering
  • 2006 - PhD, Northwestern University (USA), Electrical and Computer Engineering


  • MSc Level Machine Learning for Signal Processing (2018-)
  • MSc Level Advanced Concepts in Signal Processing (2016-2018)
  • 3rd year undergraduate, Electromagnetics, Signals and Communications 3 (2017-2019)
  • 3rd year undergraduate, Signals and Communications 3 (2015-2017)

Research Interests: 

The population increase exerts tremendous pressure on our healthcare systems and other resource needs (e.g. food, land, water, energy). Imaging methodologies are increasingly used to aid diagnoses and to build image-driven physiological models in the natural and life sciences. However, the explosion of the amount of data cannot be met by the current approaches used to analyse the generated images/volumes/movies and unravel the complicated patterns that exist. My mission, is to address these limitations by advancing the state of the art in computer vision, AI, and large language models. Our unique approach has the potential to provide efficient and affordable solutions towards sustainable healthcare systems and agriculture, of immense benefit and importance to society.

My research interests are artificial intelligence, machine learning, computer vision, image analysis, image processing, and distributed computing. Specifically within AI, my expertise lies in representation learning, generative models, privacy, fairness and causal AI. Core research applications are in computer aided diagnosis in medicine and phenotyping in biology.

Team Website:



  • Medical Image Computing and Analysis
  • Computer Vision and Machine Learning
  • Artificial Intelligence
  • Applications in the natural and life sciences