Stochastic epidemic models
Applied biostatistics and health data
Bayesian evidence synthesis
Statistical process monitoring
1. A. Apsemidis and N. Demiris (2025), Stable Survival Extrapolation using Mortality Projections, Biometrics, Volume 81, Issue 4, December 2025, ujaf159, https://doi.org/10.1093/biomtc/ujaf159
2. A. Apsemidis and N. Demiris (2025), Bayesian Evidence Synthesis for Modeling SARS-CoV-2 Transmission, Journal of the Royal Statistical Society Series A: Statistics in Society, 2025;, qnaf177, https://doi.org/10.1093/jrsssa/qnaf177
3. A. Apsemidis, S. Psarakis and J. M. Moguerza (2020) A Review of Machine Learning Kernel Methods in Statistical Process Monitoring, Computers & Industrial Engineering, Volume 142, 106376, https://doi.org/10.1016/j.cie.2020.106376
1. A. Apsemidis and N. Demiris (2025), Stable Survival Extrapolation using Mortality Projections, 2nd Conference in Biostatistics and Health Analytics, Aegina/Greece 2025
2. A. Apsemidis and N. Demiris (2023), Stochastic Epidemic Modelling of Covid-19, AUEB-NKUA Statistical Data Science Conference, Aegina/Greece 2023 (Poster)
3. A. Apsemidis and N. Demiris (2022), Stochastic Epidemic Modelling of Covid-19, 1st AUEB-NKUA-Indiana Conference on Biostatistics & Health Analytics, Aegina/Greece 2022 (Poster)
4. A. Apsemidis and N. Demiris (2022), Stochastic Epidemic Modelling of Covid-19, 34th Pan-Hellenic Statistics Conference, Athens/Greece 2022 (“Best Statistics Paper Award for Young Statisticians 2022”)
5. A. Apsemidis and S. Psarakis (2019), Statistical Process Control Chart Based on Kernel Distances, 18th Applied Stochastic Models and Data Analysis International Conference, Florence/Italy 2019
6. A. Apsemidis and S. Psarakis (2019), Using Kernel Distances in Statistical Process Control, 32nd Pan-Hellenic Statistics Conference, Ioannina/Greece 2019
7. A. Apsemidis and S. Psarakis (2018), Support Vector Machines Applied to Process Monitoring, 5th Stochastic Modeling Techniques and Data Analysis International Conference, Chania/Greece 2018
8. A. Apsemidis and S. Psarakis (2018), Support Vector Machine Models in Statistical Process Control, 2nd Conference of Young Researchers in Branches of Mathematics, Ioannina/Greece 2018
9. A. Apsemidis and S. Psarakis (2018), The Role of Support Vector Machines in Process Monitoring, 31st Pan-Hellenic Statistics Conference, Lamia/Greece 2018
1. A. Apsemidis and S. Psarakis (2020), Support Vector Machines: A Review and Applications in Statistical Process Monitoring, Wiley Online Library, Data Analysis and Applications 3: Computational, Classification, Financial, Statistical and Stochastic Methods, Volume 5, 2020, https://doi.org/10.1002/9781119721871.ch7
1. “Introduction to statistical process control” 3-hour course for AGES in Graz/Austria
“Best Statistics Paper Award for Young Statisticians 2022” by the Greek Statistical Institute.
