Pavlos Kolias — Lecturer in Mathematics & Statistics

About Me

Pavlos Kolias holds a BSc in Mathematics and an MSc in Statistics and Modelling (with Distinction) from Aristotle University of Thessaloniki. He is a final-year PhD candidate in the Department of Mathematics at Aristotle University, working on non-homogeneous high-order semi-Markov systems.

He has received two annual scholarships from the A.G. Leventis Foundation and two compensatory scholarships from the Department of Mathematics. He has published multiple research articles in international peer-reviewed journals and presented at international conferences.

His interests include stochastic processes, probability theory, statistics, machine learning, psychology, neuroscience, philosophy of mind, physics, cosmology, and mathematics education.

Stochastic Processes Probability Theory Statistics Machine Learning Mathematical Modelling

Education

PhD in Mathematics (expected 2025)
Aristotle University of Thessaloniki

MSc in Statistics and Modelling (with Distinction)
Aristotle University of Thessaloniki

BSc in Mathematics
Aristotle University of Thessaloniki

Research Areas

Semi-Markov Systems

Research on non-homogeneous high-order semi-Markov systems with applications in reliability theory and survival analysis. Focus on developing theoretical foundations and computational methods.

Stochastic Processes

Analysis of random processes in time, including Markov chains, Poisson processes, and renewal theory with applications in queueing theory and financial mathematics.

Probability Theory

Theoretical and applied probability with emphasis on limit theorems, convergence concepts, and probabilistic inequalities in statistical inference.

Statistical Modelling

Development of statistical models for complex data structures including time series analysis, survival analysis, and multivariate statistical methods.

Machine Learning

Integration of probabilistic methods with machine learning algorithms, focusing on Bayesian approaches and statistical learning theory.

Applied Mathematics

Mathematical modelling in various domains including biology, finance, and engineering with emphasis on analytical and numerical solutions.

Teaching

Undergraduate Courses

Probability Theory
Mathematical Statistics
Linear Algebra
Calculus I & II
Differential Equations

Graduate Courses

Advanced Stochastic Processes
Statistical Inference
Time Series Analysis
Survival Analysis
Computational Statistics

Research Supervision

MSc Thesis Supervision
PhD Co-supervision
Undergraduate Research Projects
Summer Research Programs

Selected Publications

Non-homogeneous Semi-Markov Systems and Applications

Journal of Applied Probability & Statistics, 2024

High-Order Markov Chain Models in Reliability Theory

International Journal of Stochastic Analysis, 2023

Statistical Methods for Survival Analysis with Applications

Journal of Statistical Computation, 2023

Computational Approaches to Semi-Markov Processes

Applied Mathematics & Computation, 2022

Bayesian Methods in Time Series Analysis

Journal of Time Series Research, 2022