PhD candidate at the University of the Aegean, developing machine-learning models for clinical decision support in intensive care — with a focus on timely, trustworthy, and clinically meaningful predictions.
Working on doctoral research in machine learning for ICU outcome prediction — focusing on leakage-safe data splitting, missing-data handling, external validation, and multi-dimensional tensor representations of temporal clinical data.
Teaching Computer Architecture laboratory courses and supporting academic work at the University of West Attica and the University of the Aegean.
I develop and evaluate predictive models for healthcare — work that connects clinical reality, statistical rigour, and computational intelligence.
My academic path combines economics, information & communication systems, and doctoral research in clinical machine learning and bioinformatics. This interdisciplinary background shapes how I approach problems: by considering both the structure of the data and the clinical context in which they are produced.
I develop machine-learning and deep-learning models for predictive analysis in healthcare using real-world clinical data — particularly in ICU settings, where uncertainty is high and decisions are time-critical.
Beyond raw performance, I care about trust — leakage-safe data splitting, missing-data handling, external validation, and clear explanations of what models actually learn.
My long-term goal is to translate research methods into clinically meaningful decision-support tools — systems that assist medical professionals in a transparent, useful, and responsible way without obscuring their judgement.
Developing and externally validating machine-learning and deep-learning models on real-world ICU data — with emphasis on temporal clinical trajectories, leakage-safe data splitting, tensor-based representations, and clinically meaningful predictions.
Reviewing machine-learning models for venous thromboembolism prediction — highlighting methodological limitations, validation gaps, and reporting practices in the current literature.
Designing evaluation pipelines for clinical prediction models — with emphasis on missing-data handling, calibration, interpretability, leakage-safe validation, and generalisability across independent cohorts.
Teaching undergraduate lab exercises in Computer Architecture, with emphasis on assembly programming, processor logic, and hands-on understanding of low-level systems.
University of the Aegean · Samos, Greece
Doctoral research on machine-learning and deep-learning methods for clinical and medical data analysis — with emphasis on ICU outcome prediction, external validation, transparency, and clinical utility.
Postgraduate studies in Information Technology at the Department of Information & Communication Systems Engineering.
Undergraduate studies in Accounting and Finance, providing a foundation in quantitative thinking, financial analysis, and structured problem solving.
University of the Aegean · Samos, Greece
Supporting laboratory courses in Java and C programming in the Department of Information & Communication Systems Engineering, including assessment of students' laboratory assignments and written work.
University of West Attica · Aigaleo, Greece
Teaching the Computer Architecture laboratory in the Department of Electrical & Electronic Engineering, School of Engineering.
ACM · SETN 2024 · Piraeus, Greece · September 2024
Thieme · Thrombosis and Haemostasis · 2024
ACM · SETN 2022 · Corfu, Greece · September 2022
Open to academic collaboration, research discussions, and conversations on machine learning, bioinformatics, and clinical decision support.
dimopoulosvtec@yahoo.gr
