ΓΙΑΚΟΥΜΑΤΟΣ ΣΤΕΦΑΝΟΣ
ΚΑΘΗΓΗΤΗΣ
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
ΤΜΗΜΑ ΛΟΓΙΣΤΙΚΗΣ ΚΑΙ ΧΡΗΜΑΤΟΟΙΚΟΝΟΜΙΚΗΣ - ΚΑΛΑΜΑΤΑ
Ε-mail: stefanos (at) uop (dot) gr
Phone: 27210-45-301
Office: Γ1.01 Νέο Κτήριο- Αντικάλαμος
Ώρες Γραφείου: Πέμπτη 13:00-14:00
Παρασκευή 11:00-12:00 - (Συνιστάται η προηγούμενη ειδοποίηση με e-mail)
Short CV
Ο Δρ. Στέφανος Γιακουμάτος είναι Καθηγητής Στατιστικής και Ποσοτικών Μεθόδων στο Τμήμα Λογιστικής και Χρηματοοικονομικής του Πανεπιστημίου Πελοποννήσου. Σπούδασε Στατιστική (Bachelor, Master και Ph.D.) στο Τμήμα Στατιστικής του Οικονομικού Πανεπιστημίου Αθηνών.
Ο Δρ. Γιακουμάτος εργάστηκε ως Στατιστικός Αναλυτής στον ιδιωτικό τομέα (Intralot SA, TNS Metrisis) και στο δημόσιο τομέα (τμήμα ΕΛ.ΣΤΑΤ., Μεθοδολογία και Ανάλυση). Έχει ευρύ διδακτικό έργο σε θέματα στατιστικής, ανάλυσης δεδομένων και ποσοτικών μεθόδων στο ΤΕΙ της Πελοποννήσου καθώς και στο Πανεπιστήμιο Πελοποννήσου.
Έχει επίσης διδάξει Στατιστική, Ανάλυση Δεδομένων Παράγωγα και Λ΄λήψη Αποφάσεων σε προπτυχιακό και μεταπτυχιακό επίπεδο στο Πανεπιστήμιο Πελοποννήσου,στο Ιόνιο Πανεπιστήμιο και στο Πάντειο Πανεπιστήμιο. Είναι Μέλος Σεπτεμβρίου του ΕΑΠ. Επιπλέον, ήταν ερευνητής στα ευρωπαϊκά ερευνητικά προγράμματα και είναι μέλος της ερευνητικής επιτροπής της Διεθνούς Οργάνωσης Εργασίας (ILO).
Τα ερευνητικά του ενδιαφέροντα περιλαμβάνουν την Μοντελοποίηση Δεδομένων σε Οικονομικά και Κοινωνικά Θέματα, Ανάλυση Χρονικών Σειρών, Τεχνικές Δειγματοληψίας, Μετρήσεις Φτώχειας
Short CV
Stefanos Giakoumatos is Professor at the Department of Accounting and Finance of the University of Peloponnese, where he teaches Statistics, Quantitative Methods, Financial Derivatives and Decision Making at undergraduate and postgraduate level. He obtained his degrees (Bachelor, Master and Ph.D.) from the Department of Statistics in Athens University of Economics and Business. In the past, Prof. Giakoumatos has been employed as Statistical Analyst in private sector (Intralot SA, TNS Metrisis) and in public sector (EL.STAT), he was researcher in European research programs and he is member of research committee of ILO (International Labor Organization).
His research interests, include Data Modelling in Finance and Social Silences, Markov Chain Monte Carlo Methods and Time Series Analysis.
Science Interest: Μοντελοποίηση Δεδομένων σε Οικονομικά και Κοινωνικά Θέματα, Λήψη απόφασης υπό καθεστώς αβεβαιότητας, Ανάλυση Χρονικών Σειρών, Τεχνικές Δειγματοληψίας, Μετρήσεις Φτώχειας
Science Interest:
Data Modelling in Finance and Social Silences, Markov Chain Monte Carlo Methods, Decision making under uncertainty, and Time Series Analysis.