Christos Diou

Office 3.5

Omirou 9, Tavros, 17778

Athens, Greece

+30-210-9549-449

Hello and welcome to my homepage. I am an Assistant Professor of Artificial Intelligence and Machine Learning at the Department of Informatics and Telematics, at the Harokopio University of Athens, Greece, where I teach courses related to artificial intelligence, machine learning, as well as computer programming. I hold a BSc and Ph.D. in Electrical and Computer Engineering from the Aristotle University of Thessaloniki.

My research focuses on the development of novel machine learning algorithms and their applications in healthcare. My recent research interests include the development of methods for the measurement and modeling of human behavior for health applications, the development of machine learning algorithms that generalize across domains (Domain Generalization) and the use of machine learning for causal effect estimation from observational data.

I have published several papers in international research journals and conferences in topics related to Artificial Intelligence, Machine Learning and their applications, while I have over 15 years of experience participating in national and international research projects. I am currently the PI for the EU-funded projects REBECCA and RELEVIUM.

news

Aug 5, 2023	We are happy to announce that our paper “Detection of Anomalies in Multivariate Time Series Using Ensemble Techniques” with A. Iliopoulos, J. Violos and I. Varlamis received the best paper award at the 2023 IEEE Big Data Service conference.
Jul 4, 2023	We will be hosting, along with Aristotelis Ballas, a tutorial session on Domain Generalization at this year’s IEEE Big Data Service conference.
Jun 21, 2023	Today I gave an invited talk entitled “The promise and risks of Artificial Intelligence in healthcare: An introductory discussion”, at the Summer school: Health Communication & Health Inequalities Across the Health Professions in Greece & the U.S.
May 10, 2023	Vasileios Gkolemis and myself will be organizing the Uncertainty meets Explainability in Machine learning workshop in this year’s ECML-PKDD conference! Please check the CFP.
Apr 18, 2023	Our paper “Integrating nearest neighbors on neural network models for treatment effect estimation” with Niki Kiriakidou has been accepted for publication in the International Journal of Neural Systems! You may find a preprint in arxiv.
Feb 28, 2023	This semester I will be teaching a 6-week synchronous joint module with Rutgers University, along with my colleague, Matthew Matsagkanis! I am grateful to our students for their participation and for supporting this activity.
Jan 30, 2023	Today I gave an invited lecture to MSc students of Karolinska Institutet on digital health, big data analysis and AI in healthcare!
Jan 3, 2023	I will be giving a talk on Interpretable Machine Learning and the DALE method at the MeVer group at the Information Technologies Institute on Jan 04, 2023. Thanks to the colleagues at the MeVer group for the invitation! You may find the slides of the talk here
Nov 1, 2022	Our paper, DALE: Differential Accumulated Local Effects for efficient and accurate global explanations by V. Gkolemis, T. Dalamagas and myself has been accepted at ACML 2022!
Sep 2, 2022	New PhD position available on methods for quantifying and mitigating AI bias. Please check here for details (in Greek).