Biography
Melina Tziomaka is a PhD candidate in the Department of Digital Systems and a member of the Computational Biomedicine Lab of the University of Piraeus. She has a degree in “Informatics and Computational Mathematics” from the Department of Mathematics of the University of Patras. In 2021 she graduated from the inter-institutional postgraduate program "Artificial Intelligence" of the Department of Digital Systems of the University of Piraeus and the Institute of Informatics and Telecommunications of NCSR "Demokritos". Her research interests focuses on the fields of machine learning and computer vision for image processing and analysis. The topic of her doctoral dissertation is “Deep Learning Models for the Diagnosis of Patients from Healthcare Data”.
Recent publications
13 publications in the lab library
Conversational Agent with Emotional Intelligence for Foreign Language Learning
In 2026 14th International Conference on Information and Education Technology (ICIET). IEEE, pp. 115–119.
An Inspectable Vision-Language and Convolutional Ensemble for Facial Pigmented Lesion Diagnosis
In Innovating Bioengineering Through Next-Generation Computational Approaches. Springer Nature Switzerland, pp. 295–312.
Multimodal Affective Modeling in an LLM-based Intelligent Tutoring System for Foreign Language Learning
In Proceedings of Computational Affective Science (CAS) @ LREC 2026. European Language Resources Association (ELRA), pp. 190–200.
A Fine-Tuned Vision Language Model Framework for Medical Forensic Image Analysis
In Artificial Intelligence Applications and Innovations. Springer Nature Switzerland, pp. 326–339.
Advanced Multi-class Wound Segmentation Using EfficientNet UNet
In Joint 20th Nordic-Baltic Conference on Biomedical Engineering & 24th Polish Conference on Biocybernetics and Biomedical Engineering. Springer Nature Switzerland, pp. 423–440.
Vitiligo Skin Segmentation: An Image-Based Unsupervised Learning Approach
In Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024). Springer Nature Singapore, pp. 246–255.