Stavros N. Nikolopoulos, received his PhD degree in Biomedical Engineering from the School of Electrical and Computer Engineering in National Technical University of Athens in Greece. He did post-doctoral studies in the Centre de Recherche de l'ICM, INSERM UMRS 975 - CNRS UMR 7225, Hôpital de la Pitié-Salpêtrière, in Paris, France. Currently he works in the Directorate of Informatics of University of Peloponnese, where he is conducting technical, educational and research activities.

His main research activities regard non linear analysis of timeseries generated from experimental measures,  on biological systems of heart and brain, and of electromagnetic geophysical signals as well. He has been participated in European research programs, such as the Program of Industrial Research Development with code 96BE85 or the  FP7 Project EPILEPSIAE (Evolving Platform for Improving Living Expectation of Patients Suffering from Ictal Events, Grant No 211713). In the latter he was one of the members developed EPILAB, a software package for studies on the prediction of epileptic seizures.

He also participated in the European research program EPEAEK/PYTHAGORAS 70/3/7357, where he conducted research in experimental geophysical signals for identifying precursors to imminent severe land earthquakes. His article “A unified approach of catastrophic events” Natural Hazards and Earth System Sciences 4: 615 – 631 (2004), has been entered in the collective Book entitled “Models and Application of Chaos in Modern Sciences”, edited by Dr Elhadj Zeraoulia, Science Publishers, USA, 2011.  

He has published more than 40 papers in the field of Non linear Timeseries Analysis, obtained from experimental data in international journals and full review conferences.

From 2001 up to 2017, he has been working in the Ministry of Public Order and Citizen Protection in the sector of Cryptology and Information Assurance. He is co-beneficiary in a patent entitled: Communication Security in Networks with the aid of random check, with number: ΔΕ 20030100537. Athens 29-12-2003.

Data process and analysis.

Data mining and prediction

Linear and nonlinear timeseries analysis and prediction, applied to natural experimental data.

Biostatistics