Lehrgebiet: Theoretische Informatik und künstliche Intelligenz
Büro: 01.214
Labor: 04.105
Telefon: +49 208 88254-806
E-Mail:
Ioannis Iossifidis studierte Physik (Schwerpunkt: theoretische Teilchenphysik) an der Universität Dortmund und promovierte 2006 an der Fakultät für Physik und Astronomie der Ruhr-Universität Bochum.
Am Institut für Neuroinformatik leitete Prof. Dr. Iossifidis die Arbeitsgruppe Autonome Robotik und nahm mit seiner Forschungsgruppe erfolgreich an zahlreichen, vom BmBF und der EU, geförderten Forschungsprojekten aus dem Bereich der künstlichen Intelligenz teil. Seit dem 1. Oktober 2010 arbeitet er an der HRW am Institut Informatik und hält den Lehrstuhl für Theoretische Informatik – Künstliche Intelligenz.
Prof. Dr. Ioannis Iossifidis entwickelt seit über 20 Jahren biologisch inspirierte anthropomorphe, autonome Robotersysteme, die zugleich Teil und Ergebnis seiner Forschung im Bereich der rechnergestützten Neurowissenschaften sind. In diesem Rahmen entwickelte er Modelle zur Informationsverarbeitung im menschlichen Gehirn und wendete diese auf technische Systeme an.
Ausgewiesene Schwerpunkte seiner wissenschaftlichen Arbeit der letzten Jahre sind die Modellierung menschlicher Armbewegungen, der Entwurf von sogenannten «Simulierten Realitäten» zur Simulation und Evaluation der Interaktionen zwischen Mensch, Maschine und Umwelt sowie die Entwicklung von kortikalen exoprothetischen Komponenten. Entwicklung der Theorie und Anwendung von Algorithmen des maschinellen Lernens auf Basis tiefer neuronaler Architekturen bilden das Querschnittsthema seiner Forschung.
Ioannis Iossifidis’ Forschung wurde u.a. mit Fördermitteln im Rahmen großer Förderprojekte des BmBF (NEUROS, MORPHA, LOKI, DESIRE, Bernstein Fokus: Neuronale Grundlagen des Lernens etc.), der DFG («Motor‐parietal cortical neuroprosthesis with somatosensory feedback for restoring hand and arm functions in tetraplegic patients») und der EU (Neural Dynamics – EU (STREP), EUCogII, EUCogIII ) honoriert und gehört zu den Gewinnern der Leitmarktwettbewerbe Gesundheit.NRW und IKT.NRW 2019.
ARBEITS- UND FORSCHUNGSSCHWERPUNKTE
- Computational Neuroscience
- Brain Computer Interfaces
- Entwicklung kortikaler exoprothetischer Komponenten
- Theorie neuronaler Netze
- Modellierung menschlicher Armbewegungen
- Simulierte Realität
WISSENSCHAFTLICHE EINRICHTUNGEN
- Labor mit Verlinkung
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LEHRVERANSTALTUNGEN
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PROJEKTE
- Projekt mit Verlinkung
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WISSENSCHAFTLICHE MITARBEITER*INNEN
Felix Grün
Büro: 02.216 (Campus Bottrop)
Marie Schmidt
Büro: 02.216 (Campus Bottrop)
Aline Xavier Fidencio
Gastwissenschaftlerin
Muhammad Ayaz Hussain
Doktorand
Tim Sziburis
Doktorand
Farhad Rahmat
studentische Hilfskraft
AUSGEWÄHLTE PUBLIKATIONEN
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2009
2.Tuma, M; Iossifidis, Ioannis; Schöner, G
Temporal stabilization of discrete movement in variable environments: An attractor dynamics approach Proceedings Article
In: 2009 IEEE International Conference on Robotics and Automation, S. 863–868, IEEE, 2009, ISBN: 978-1-4244-2788-8.
Abstract | Links | BibTeX | Schlagwörter: attractor dynamics approach, Autonomous robotics, Dynamical systems, hopf oscillator
@inproceedings{Tuma2009b,
title = {Temporal stabilization of discrete movement in variable environments: An attractor dynamics approach},
author = {M Tuma and Ioannis Iossifidis and G Schöner},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5152562},
doi = {10.1109/ROBOT.2009.5152562},
isbn = {978-1-4244-2788-8},
year = {2009},
date = {2009-05-01},
urldate = {2009-05-01},
booktitle = {2009 IEEE International Conference on Robotics and Automation},
pages = {863--868},
publisher = {IEEE},
abstract = {The ability to generate discrete movement with distinct and stable time courses is important for interaction scenarios both between different robots and with human partners, for catching and interception tasks, and for timed action sequences. In dynamic environments, where trajectories are evolving online, this is not a trivial task. The dynamical systems approach to robotics provides a framework for robust incorporation of fluctuating sensor information, but control of movement time is usually restricted to rhythmic motion and realized through stable limit cycles. The present work uses a Hopf oscillator to produce discrete motion and formulates an online adaptation rule to stabilize total movement time against a wide range of disturbances. This is integrated into a dynamical systems framework for the sequencing of movement phases and for directional navigation, using 2D-planar motion as an example. The approach is demonstrated on a Khepera mobile unit in order to show its reliability even when depending on low-level sensor information.},
keywords = {attractor dynamics approach, Autonomous robotics, Dynamical systems, hopf oscillator},
pubstate = {published},
tppubtype = {inproceedings}
}
The ability to generate discrete movement with distinct and stable time courses is important for interaction scenarios both between different robots and with human partners, for catching and interception tasks, and for timed action sequences. In dynamic environments, where trajectories are evolving online, this is not a trivial task. The dynamical systems approach to robotics provides a framework for robust incorporation of fluctuating sensor information, but control of movement time is usually restricted to rhythmic motion and realized through stable limit cycles. The present work uses a Hopf oscillator to produce discrete motion and formulates an online adaptation rule to stabilize total movement time against a wide range of disturbances. This is integrated into a dynamical systems framework for the sequencing of movement phases and for directional navigation, using 2D-planar motion as an example. The approach is demonstrated on a Khepera mobile unit in order to show its reliability even when depending on low-level sensor information.1.Tuma, Matthias; Iossifidis, Ioannis; Schöner, Gregor
Temporal Stabilization of Discrete Movement in Variable Environments: An Attractor Dynamics Approach Proceedings Article
In: Proc. IEEE International Conference on Robotics and Automation ICRA '09, S. 863–868, Kobe, Japan, 2009.
Abstract | BibTeX | Schlagwörter: attractor dynamics approach, Autonomous robotics, Dynamical systems, hopf oscillator
@inproceedings{Tuma2009,
title = {Temporal Stabilization of Discrete Movement in Variable Environments: An Attractor Dynamics Approach},
author = {Matthias Tuma and Ioannis Iossifidis and Gregor Schöner},
year = {2009},
date = {2009-01-01},
booktitle = {Proc. IEEE International Conference on Robotics and Automation ICRA '09},
pages = {863--868},
address = {Kobe, Japan},
abstract = {The ability to generate discrete movement with distinct and stable time courses
is important for interaction scenarios both between different robots and with human partners,
for catching and interception tasks, and for timed action sequences.
In dynamic environments, where trajectories are evolving on-line, this is not a trivial task.
The dynamical systems approach to robotics provides a framework for robust
incorporation of fluctuating sensor information, but control of movement time is usually
restricted to rhythmic motion and realized through stable limit cycles. The present work
uses a Hopf oscillator to produce discrete motion and formulates an on-line adaptation rule
to stabilize total movement time against a wide range of disturbances. This is integrated into
a dynamical systems framework for the sequencing of movement phases and for directional navigation, using 2D-planar motion
as an example. The approach is demonstrated on a Khepera mobile unit in order to show its
reliability even when depending on low-level sensor information.},
keywords = {attractor dynamics approach, Autonomous robotics, Dynamical systems, hopf oscillator},
pubstate = {published},
tppubtype = {inproceedings}
}
The ability to generate discrete movement with distinct and stable time courses
is important for interaction scenarios both between different robots and with human partners,
for catching and interception tasks, and for timed action sequences.
In dynamic environments, where trajectories are evolving on-line, this is not a trivial task.
The dynamical systems approach to robotics provides a framework for robust
incorporation of fluctuating sensor information, but control of movement time is usually
restricted to rhythmic motion and realized through stable limit cycles. The present work
uses a Hopf oscillator to produce discrete motion and formulates an on-line adaptation rule
to stabilize total movement time against a wide range of disturbances. This is integrated into
a dynamical systems framework for the sequencing of movement phases and for directional navigation, using 2D-planar motion
as an example. The approach is demonstrated on a Khepera mobile unit in order to show its
reliability even when depending on low-level sensor information.