Students' Supervision

PhD Students

• Lefkakis C., 2026- , Uncertainty-Aware and Causally Informed Explainability for Neural Networks, co-supervised by Prof. Dimitris Karlis, Department of Statistics, AUEB.
• Thomadakis C., 2015-2022, Statistical modelling of CD4 cell count evolution in HIV-positive individuals before and after antiretroviral treatment initiation under missing data due to various dropout mechanisms, co-supervised by Prof. Giota Touloumi, School of Medicine, NKUA.
• Koki C., 2013-2021, Bayesian Modelling and Estimation for Complex Multiparameter Problems with Real Applications, co-supervised by Associate Prof. Ioannis Vrontos, Department of Statistics, AUEB.

MSc Students

• Tsetso A., 2026-2027, Bayesian Covariate Selection for Generalized Linear Models with Extension to the Multi-parameter Estimation of Prevalence Framework.
• Kitsios C., 2026-2027, Structural Causal Models: Causal Discovery and Inference.
• Kotsifakou P., 2026-2027, Statistical Modelling and Inference for Multivariate Time-Series Data.
• Lefkakis C., 2025-2026, From Deterministic to Uncertainty-Aware Neural Networks: A From-Scratch Study on MNIST and Fashion-MNIST.
• Vidali E., 2025-2026, Statistical and Machine Learning Methods for Classification with Application to Archaeometry.
• Kampa F., 2025-2026, Bayesian Spatio-temporal Modelling, Mapping and Prediction of Disease Risk.
• Liolis F., 2024-2025, Bayesian Inference for Discrete-Valued Mixture Models and Finite State-Space Hidden Markov Models.
• Mavrikios M., 2023-2024, Statistical Learning Through Bayesian Additive Regression Trees.
• Koutsiouroumpa O., 2020-2021, Bayesian Inference for Linear and Generalised Linear Models.
• Zacharias C., 2019-2020, Literature Review of the Generalized Additive Model for Location, Scale and Shape.
• Georgopoulou V., 2019-2020, Bayesian Unit Root Testing for Autoregressive Models.
• Paraskevopoulou C., 2018-2019, Variable Selection in Linear and Generalised Linear Models.
• Karampateas A., 2017-2018, Predictive Regressions: Variable Selection and the Complete Subset Approach.
• Katsianos V., 2017-2018, Likelihood-Based Inference and Model Selection for Discrete-Time Finite State-Space Hidden Markov Models.
• Tsiampalis T., 2017-2018, Bayesian Inference for Cure Rate Models.
• Mpaltouka M., 2016-2017, Bayesian Inference for Threshold Regression Models.
• Markoulidakis A., 2016-2017, Ordinary and Bayesian Lasso for Regression Models.
• Kikeri M., 2014-2015, Bayesian Meta-analysis of Count Data.
• Thomadakis C., 2013-2014, Bayesian Inference for Time-to-event Data.
• Konstantinou M., 2012-2013, Sequantial Monte Carlo Methods.
• Koki C., 2011-2012, Poisson and Binomial Processes with Application to Astrophysical Data.
• Andreopoulos P., 2011-2012, A Study of Spatial Models with Application to Disease Mapping.
• Lamprinakou F., 2011-2012, Markov chain Monte Carlo Algorithms for Bayesian Estimation of Quantile Regression Models.
• Mponi I., 2010-2011, Bayesian Inference for Generalised Linear Models.
• Sitokonstantinou V., 2009-2010, Bayesian inference for Hidden Markov Models.
• Chanialidis C., 2009-2010, Inference and Variable Selection for Quantile Regression Models.
• Papaioanou N., 2007-2008, Maximum Likelihood Estimation for Poisson Mixture Models via the EM Algorithm.
• Koutsourelis A., 2005-2006, Bayesian Inference for Hidden Markov Models with Application to Financial Econometrics.