2810379750 bmi@hmu.gr


Head of the BMI Lab

  • Tsiknakis Manolis, Ph.D., Visiting Research Professor, Computational BioMedicine Lab, FORTH

Collaborating Researchers

  • Koumakis Lefteris, Ph.D., Collaborating Researcher, Computational BioMedicine Lab, FORTH
  • Marias Konstantinos, Ph.D., Collaborating Researcher, Head of the Computational BioMedicine Lab, FORTH

Software Engineers

Doctoral Students

  • Chatzaki Roula, M.Sc.
    • Research topic: “A multimodal approach for the assessment of mental stress based on the analysis of physiological signals”
  • Chatzimina Maria, M.Sc.
    • Research topic: “Conversational agents as a new diagnostic tool: a proof-of-concept study in the field of palliative care
  • Gkikas Stefanos, M.Sc.
    • Research topic: “A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods”

Postgraduate students

  • Pavlidou Elsa, B. Sc.
    • Thesis: “Multimodal pain intensity assessment based on physiological biosignals
    • Description: The purpose of the thesis is to develop computational, predictive models and conduct validation experiments by using multimodal physiological biosignals such as Electrocardiogram, Electromyogram and Galvanic Skin Response.
  • Pityanou Konstantina, B. Sc.
    • Thesis: “A set of mechanisms to collect, display and analyze quality metrics of Research Objects as a method to augment reproducibility and openness in bioinformatics research
    • Description: The purpose of the thesis is to provide a solution the problem of mis-attribution, profile building and the evaluation method of a tool, data or workflow that exists in a repository of the bioinformatics community. Those issues will be implemented in the OpeBio.eu platform, by adding a rich set of quality metrics that will be displayed in an intuitive user interface. Also, it will allow the proper citation of users, when it comes to their creations, whether those are submitted research papers, tool repositories or Workflow Management Systems. Last, it will provide clear and objective indications of the expertise and the scientific activity of users, according to their quality and quantity of contributions to the platform.

Undergraduate students

  • Adam Giannis
    • Thesis: “Development of an app for measuring Heart Rate and Heart Rate Variability using wearable sensors
  • Garganourakis Vasileios, Ph. D., Phycisian
    • Thesis: “Emotion detection through video and biosignal analysis
  • Garefalaki Magdalini
    • Thesis: “Pain detection through video analysis and biosignal analysis
  • Gialelaki Irini
    • Thesis: “Design and development of an embodied conversational agent (ECA) and its application for improving the well-being of cancer patients
  • Dogramatzaki Zaxarenia
    • Thesis: “Assessment of emotional state through video and biosignal analysis”
  • Katsanevakis Aris
    • Thesis: “Usage of deep learning techniques for the development of predictive models for future disease risks by analyzing data from patients’ electronic health records
  • Papadaki Maria
    • Thesis: “Analysis of freezing of gait (FOG) using smart wearable insoles
  • Pronoitis Giorgos
    • Thesis: “Design and implementation of a Personal Health Record (PHR) system
  • Stavrakakis Marinos
    • Thesis: “Development of an app for assessing the user’s cognitive ability
  • Strataki Despina
    • Thesis: “Architectures and platforms for connected smart objects