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

  • 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.
  • Tsichlaki Stella, B.Sc.
    • Thesis: “T1D Hypoglycemia Prediction Based on Continuous Glucose Monitoring and Heart Rate Variability“.
    • Description: In this thesis will be examined the use of biosignals and other measurements provided by  a wearable device (HRV, SpO2, temperature, exercise, steps and sleep quality) along parameters to be provided by the user herself/himself (meals during day, insulin type and dose, and psychoemotional status captured though user-filled questionnaires) for the development of a hypoglycemia predictive model. In addition, a diabetes management mobile app will be developed, and will be used for the data collection from the patient, i.e finger-stick glucose measurements, insulin doses, food and exercise, as well as mood.

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
  • 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”
  • Kalogeraki Maria
    • Thesis: “Pain detection through bio – signal analysis
  • Karakatsanov Maxim
    • Thesis: “Online and interactive visualization of multiple gene regulatory networks
  • 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
  • Mamouridou Panagiota
    • Thesis: “Design and Development an environment for a Personal Electronic Health Record
  • Papadaki Maria
    • Thesis: “Analysis of freezing of gait (FOG) using smart wearable insoles
  • Stavrakakis Marinos
    • Thesis: “Development of an app for assessing the user’s cognitive ability
  • Strataki Despina
    • Thesis: “Architectures and platforms for connected smart objects