UOP staff
ΒΛΑΧΟΣ ΔΗΜΗΤΡΙΟΣ
ΚΑΘΗΓΗΤΗΣ
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
Ε-mail: dvlachos (at) uop (dot) gr
Γραφείο: Θέση Σέχι - 2ος όροφος
Ώρες Γραφείου: 11:00 - 14:00 - (Συνιστάται πρότερη επικοινωνία με το διδάσκοντα)
Σύντομο Βιογραφικό ΣημείωμαΟ Δημήτριος Σ. Βλάχος έλαβε το διδακτορικό του από τη Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών του Εθνικού Μετσόβιου Πολυτεχνείου. Στη συνέχεια εργάστηκε για 2 χρόνια ως μεταδιδακτορικός ερευνητής στο Ινστιτούτο Πυρηνικής Μηχανικής του Ερευνητικού Κέντρου «Δημόκριτος» και για 8 χρόνια ως ερευνητής στο Ελληνικό Κέντρο Θαλάσσιων Ερευνών. Από το 2007 έως το 2013 εργάζεται ως Επίκουρος Καθηγητής στο Τμήμα Επιστήμης και Τεχνολογίας Υπολογιστών του Πανεπιστημίου Πελοποννήσου και από το 2013 έως το 2020 ως Αναπληρωτής Καθηγητής στο ίδιο Τμήμα. Από το 2020 έως σήμερα είναι Αναπληρωτής Καθηγητής στο Τμήμα Οικονομικών Επιστημών του Πανεπιστημίου Πελοποννήσου. Τα ερευνητικά του ενδιαφέροντα εστιάζονται στη Θεωρία Διακριτών Μεταβλητών, Πολύπλοκα Δίκτυα, Εξελικτικούς Αλγόριθμους, Μοντελοποίηση Φυσικών και Κοινωνικοοικονομικών Φαινομένων. Τέλος, είναι συγγραφέας περισσότερων από 110 εργασιών δημοσιευμένων σε Επιστημονικά Περιοδικά και Πρακτικά Συνεδρίων.
Short CVDimitrios S. Vlachos received his PhD degree from the School of Electrical and Computer Engineering at the National Technical University of Athens. After that he worked for 2 years as a post-doc researcher at the Institute of Nuclear Engineering at the Research Center ‘Demokritos’ and for 8 years as a researcher at the Hellenic Center for Marine Research. From 2007 until 2013 he is working as an Assistant Professor at the Department of Computer Science and Technology, University of Peloponnese and from 2013 until 2020 as Associate Professor at the same Department. From 2020 until today, he is an Associate Professor at the Department of Economics, University of Peloponnese. His research interests focus on Discrete Variational Theory, Complex Networks, Evolutionary Algorithms, Physical and Socioeconomic Modeling. Prof Vlachos is the author of more than 110 # papers published in Scientific Journals and Conference Proceedings.
Επιστημονικά Ενδιαφέροντα:
  • Αριθμητική επίλυση της μονοδιάστατης και διδιάστατης εξίσωσης του Schrödinger. με εφαρμογές σε προβλήματα σκέδασης και σε προβλήματα ιδιοτιμών.
  • Αριθμητική επίλυση προβλημάτων μακρού χρόνου ολοκλήρωσης.
  • Αριθμητική γεωμετρική ολοκλήρωση
  • Διακριτή θεωρία μεταβολών
  • Εφαρμογή γενετικών αλγόριθμων για επίλυση πολύπλοκων προβλημάτων βελτιστοποίησης.
  • Μελέτη συντονισμού σε πολύπλοκα συστήματα.
  • Προσομοίωση εξελικτικών διαδικασιών σε πολύπλοκα δίκτυα.
  • Ανάλυση δυναμικών ιδιοτήτων πολύπλοκων δικτύων
  • Προσομοίωση φυσικών συστημάτων
  • Αλγοριθμικό εμπόριο
  • Οικονοφυσική
Science Interest:
  • Numerical solution of ordinary differential equations
  • Long term numerical integration
  • Numerical geometric integration
  • Discrete variational principles
  • Evolutionary algorithms
  • Complex networks and complex systems
  • Algorithmic trading
  • Applications of machine learning techniques in physical and financial sciences
  • Cortical layer algorithms
  • Econophysics
  • Modeling of Social and Economic Systems