Principal Investigators
Catherine Kyrtsou is Professor of MacroFinance, Chair of the Department of Economics at the University of Macedonia, in Greece and Deputy Director of CAC at the ΙΧΧΙ Institut Rhône-Alpin des Systèmes Complexes in Lyon. She is visiting Professor at the Hellenic Open University and the International Hellenic University, Associate Researcher at the University of Paris 10, Associate Member of the Institut des Systèmes Complexes de Paris, Ile-de-France, and Member of the Euro Area Business Cycle Network, the Society for Economic Measurement, the Cliometric Society, and the Complex Systems and Applications group (COSA) of the National Centre of Scientific Sciences “Demokritos” in Athens. She was visiting Professor at the University of Montpellier and Associate Researcher at the University of Strasbourg, while she offered her expertise in research projects funded by the ΙΧΧΙ Institut Rhône-Alpin des Systèmes Complexes and the University of Luxembourg. Moreover, she acts as expert evaluator for the US National Science Foundation, the H.F.R.I and the European Commission, serves as book reviewer for the Routledge Economics list, and participates in co-funded European Union projects. She is member of the Editorial Board of Annals of Financial Economics and Journal of Behavioural and Experimental Finance, Associate Editor of Brussels Economic Review, European Journal of Finance, International Review of Financial Analysis, Journal of Economic Asymmetries, Guest Associate Editor of the International Journal of Bifurcation and Chaos.
Angeliki Papana is Postdoctoral Researcher. Her BSc is in Mathermatics (AUTh), while the PhD is in nonlinear statistical analysis of biological time series. Her research interest include dynamical systems, chaos, information theory, mathematical physics, hypothesis testing, signal analysis, surrogate data and Monte Carlo simulations. She has been involved with the development of non-linear and non-parametric methods of time series analysis, correlation and Granger causality measures, resampling methods and randomization tests for independence and non-linearity. Emphasis is placed on her recent research activities in developing multivariate direct causality methods applied to non-stationary data and the study of complex networks. Applications of the above have been performed on financial time series innovating methodologically but also with the respect to the application itself.
Researchers
Konstantinos Angelou is a Postdoctoral Researcher. He holds a BSc in Physics (AUTh) and an MSc in Computational Physics (AUTh) during which he was introduced to network theory. His PhD holds the title “Applications of statistical physics theories on research and innovation networks”. His research interests focus on the structural analysis and evolution of single- and multi-layer networks, by applying various structural measures and theories such as k-shell decomposition and percolation.
Christina Mikropoulou is a Postdoctoral Researcher. She holds a BSc in Economics, MBA in Financial Management (UoM) and a PhD from the Department of Economics (UoM). Her research lies upon the study of interdependencies between Financial Markets and the Macroeconomy. She has worked as a PhD Researcher in Supervisory Statistics Division at the European Central Bank and as a Junior Researcher in the European Territorial Cooperation Program “Interregional Cooperation at Scientific Computing in Interdisciplinary Science”. Her main research interests include nonlinear time series analysis, Behavioral Economics and Finance, Financial Instability, Business Cycles, and Complex Networks.
Elsa Siggiridou is a PhD student at the Department of Electrical and Computer Engineering (AUTh) and her research is on connectivity and time series networks. She holds BSc and MSc in Mathematics (AUTh). Her scientific background is associated with time series analysis, Granger causality and dimension reduction methods with applications on brain connectivity and finance.
Advisors
Dimitris Kugiumtzis is Professor at the Department of Electrical and Computer Engineering (AUTh). His BSc is in Mathematics (AUTh), while he has MSc and PhD on Informatics (University of Oslo). His main research area is time series analysis in conjunction with dynamical systems, chaos, complexity, computational statistics and data mining. Applications extend from neuroscience to climate and finance. He has published over 60 journal papers and many international and national proceedings papers. He has participated in several national and European research projects, acted as EU evaluator and regular reviewer for a number of journals.
Ariadni Papana Dagiasis is Associate Professor at the Department of Mathematics (Cleveland State University). Her BSc is in Mathermatics (AUTh), while MSc and PhD is in statistics and the comprehensive analysis of microarray data (Case Western Reserve University, Cleveland). Her research interest focuses on high dimensional data, data mining, dimension reduction, graphical methods, variable selection, design and analysis of experiments, functional data and clinical trials. Applications have been performed in Genomics, Medicine, Supply Chain Management and Economics. Her scholarly work seeks to improve understanding of specialized important statistical questions that arise in dealing with real data and real life applications. More recent collaborative works deals with machine learning variable selection applications to economic data.
Alkiviadis Tsimpiris is Assistant Professor at the Department of Computer, Informatics and Telecommunications Engineering (International Hellenic University). His BSc is in Physics, University of Ioannina (UoI). His first PhD is in Molecular Mechanics/Dynamics and Quantum Chemistry (Department of Chemistry, UoI) and the socond PhD on data mining in time series databases (Department of Mathematical, Physical and Computational Sciences, AUTh). His research interests include information technology, data bases, time series analysis, feature selection, networks, classification from Granger causality, change point detection in multivariate time series, data mining in oscillating time series, deep learning models, video database analysis and web / local applications for time series analysis.