|Research Engineer | PhD Candidate
Department of Electrical and Computer Engineering
I am a dedicated and results-driven research engineer with expertise in computational biomedical engineering. With a strong academic background and practical experience, I have successfully contributed to research projects in the fields of medical image processing, biometric data analysis, and wearable technology. My passion lies in applying advanced machine learning and artificial intelligence techniques to solve complex healthcare challenges.
Ph.D. Candidate in Computational BioMedicine, Hellenic Mediterranean University (HMU), Greece (Expected Graduation: 2025)
BEng – MEng, Dept. of Electrical Engineering and Computer Science, University of Patras, Greece
Research Engineer, Computational BioMedicine Laboratory, ICS FORTH, Greece (November 2019 – Present)
Visiting Research Engineer, KTH Royal Institute of Technology, Sweden (September 2021 – October 2021)
Research Projects Participation
CARDIOCARE: EU-funded project focusing on personalized care for elderly breast cancer patients, utilizing eHealth applications, wearable sensors, and biomarkers. Involved in integrating wearables and mobile health application data for monitoring patients’ response and developing deep learning approaches for early diagnosis and management of cardiotoxicity. (2022 – Present)
See Far: Development of smart glasses integrating augmented reality technologies for older adults. Led the design and implementation of algorithmic approaches for emotional and cognitive processes analysis using eye-tracking features. Conducted experimental trials, designed components, and explored machine learning methods. (2019 – 2022)
Smart Insole: Conducted research on gait characteristics analysis among Parkinson’s patients using wearable sensors. Developed machine learning algorithms, collaborated with clinical experts, and assisted in the design and development of 3D printed testing equipment. (2020 – 2022)
Full CV can be found here.
My current research interests are in the following areas:
Machine Learning and Artificial Intelligence in Healthcare: Exploring the application of advanced machine learning and artificial intelligence techniques to analyze biomedical data, improve diagnostic accuracy, develop predictive models, and enhance treatment outcomes in various healthcare domains.
Computational Biomedicine: Investigating the development and utilization of computational models, algorithms, and tools for analyzing medical data, such as medical images, physiological signals, and genomic data, to gain deeper insights into disease mechanisms, enable personalized medicine, and support clinical decision-making.
Wearable and Ubiquitous Healthcare Technologies: Exploring the design, development, and evaluation of wearable devices, sensors, and mobile health applications for continuous monitoring, early detection, and intervention in chronic diseases, aging-related conditions, and mental health disorders.
Data-driven Healthcare Systems: Investigating the use of big data analytics, data mining, and data-driven approaches to extract meaningful insights from large-scale healthcare datasets, optimize healthcare processes, improve healthcare delivery, and support evidence-based decision-making.