AUEB-Stats @ΣtatMod 2025

From September 26 to 28, AUEB-Stats participated in the "ΣtatMod 2025: Statistical Modeling with Applications" Conference, hosted at the University of Piraeus under the auspices of the Department of Statistics and Insurance Science.

With strong participation across sessions, our faculty and researchers delivered talks, chaired discussions, and contributed to a wide spectrum of topics, from Bayesian inference and machine learning to time series, probability models, stochastic modeling, and statistical process monitoring.

AUEB-Stats' speakers included:

  • Petros Dellaportas (Professor, AUEB-Stats & UCL) – Gaussian Invariance in Markov Chain Monte Carlo
  • Ioannis Ntzoufras (Professor, AUEB-Stats) – Bayesian Signature Authenticity Validation
  • Panagiotis Papastamoulis (Assistant Professor, AUEB-Stats) – Bayesian inference and Cure Rate Models
  • Dimitris Karlis (Professor, AUEB-Stats) – Non-parametric random coefficient integer autoregressive models
  • Anna Nalpantidi (PhD Student, AUEB-Stats) – Bivariate autoregressive model for ordinal time series
  • Vassilis Vasdekis (Professor, AUEB-Stats, Rector, Athens University of Economics and Business) – Bias correction in random effects models with sparse binary responses
  • Michael Zazanis (Professor, AUEB-Stats) – Coverage Processes with Infinitely Divisible Marginals and the Ornstein–Uhlenbeck Process
  • Vasilis Chasiotis (Assistant Professor, AUEB-Stats) – Supervised statistical (machine) learning for domain estimation with business survey data
  • Panagiotis Tsiamyrtzis (Associate Professor, AUEB-Stats) – Self-Starting Shiryaev (3S): A Bayesian Change Point Model for Online Monitoring of Short Runs

The conference was also especially meaningful as it honored Professor Markos V. Koutras on the occasion of his retirement.

A heartfelt thank you to the Organizing Committee and the University of Piraeus for hosting such a well-organized and stimulating Event.

To our AUEB-Stats faculty and researchers: we, always, are so proud of you; thank you!