Sotirios Chatzis

Assistant Professor and Lab Director: Dr Sotirios Chatzis is a Computer Engineering graduate and holds a PhD in Statistical Machine Learning. He was lucky to study alongside renowned Bayesian statistics experts with the Centre for Computational Science, University of Miami, USA. He worked with Imperial College London as a Senior Research Associate for three years, before becoming Faculty with the Cyprus University of Technology. His research interests lie in the fields of hierarchical graphical models and approximate Bayesian inference. Characteristic application areas include recommendation systems, natural language understanding, dialogue systems, speech understanding and video understanding. Since 2016, he serves each consecutive year in the program committee of the two most prominent international venues in Machine Learning, namely ICML and NIPS. This is a great honour that vouches for the recognition of his work among his peers, and the bleeding edge nature of it. He also serves as PI of several research projects funded by the European Commission and the Research Promotion Foundation of Cyprus.

Research Fellows

Harris Partaourides

Postdoctoral Research Assistant: Dr. Partaourides received his Ph.D. degree in Machine Learning from Cyprus University of Technology, in 2018 with a focus on Bayesian Inference Techniques for Deep learning. Dr. Partaourides research interests are focused on machine learning theory and methodologies, specifically hierarchical Bayesian models and deep hierarchical feature extractors. In addition, he primarily worked with supervised and unsupervised learning for modeling data with spatial and temporal dynamics while keeping an interest in reinforcement learning. He has published 4 papers in top-tier venues, serves as a reviewer for major Machine Learning venues (Transactions on Signal Processing, Pattern Recognition) and participated in HORIZON 2020 research projects.

Kyriacos Tolias

PhD Student: Kyriacos’ work focuses on variational inference techniques for improving natural language processing pipelines, including question-answering networks and document summarization algorithms based on sequence-to-sequence models.


Nick Zizos

PhD Student: Nick’s work focuses on deep learning for memory-efficient and uncertainty-aware deep learning with multimodal data.


Ioannis Kourouklides

PhD Student: Ioannis’ work focuses on deep learning for memory-efficient and uncertainty-aware deep learning with multimodal data.

Christos Kleanthous

PhD Student: Christos’ work deals with hierarchical graphical models treated via variational inference arguments, focusing on models tailored to the problem of tax audit case selection.


Tasos Antoniadis

PhD Student: Tasos’ work focuses on Deep Gaussian Processes and variational inference.



Andreas Voskou

PhD Student: Andreas’ work revolves around the theme of establishing memory-efficient deep learning pipelines by leveraging nonparametric Bayesian arguments and scalable MCMC schemes.


Ioannis Armenakis

PhD Student: Ioannis’ work focuses on statistical inference techniques applied to eco-friendly ship design.