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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2010
5.Zibner, Stephan K U; Faubel, Christian; Iossifidis, Ioannis; Schöner, Gregor
Scene Representation Based on Dynamic Field Theory: From Human to Machine Artikel
In: Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience, 2010.
Links | BibTeX | Schlagwörter: dynamic neural field, Dynamical systems, man machine interaction, scene representation, speech recognition
@article{Zibner2010a,
title = {Scene Representation Based on Dynamic Field Theory: From Human to Machine},
author = {Stephan K U Zibner and Christian Faubel and Ioannis Iossifidis and Gregor Schöner},
doi = {10.3389/conf.fncom.2010.51.00019},
year = {2010},
date = {2010-01-01},
journal = {Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience},
keywords = {dynamic neural field, Dynamical systems, man machine interaction, scene representation, speech recognition},
pubstate = {published},
tppubtype = {article}
}
4.Zibner, Stephan S K U; Faubel, Christian; Iossifidis, Ioannis; Schöner, Gregor
Scene Representation for Anthropomorphic Robots: A Dynamic Neural Field Approach Proceedings Article
In: ISR / ROBOTIK 2010, VDE VERLAG GmbH, Munich, Germany, 2010.
Abstract | Links | BibTeX | Schlagwörter: Autonomous robotics, dynamic neural field, Dynamical systems, man machine interaction, scene representation, speech recognition
@inproceedings{Zibner2010ab,
title = {Scene Representation for Anthropomorphic Robots: A Dynamic Neural Field Approach},
author = {Stephan S K U Zibner and Christian Faubel and Ioannis Iossifidis and Gregor Schöner},
url = {http://www.vde-verlag.de/proceedings-en/453273138.html},
year = {2010},
date = {2010-01-01},
booktitle = {ISR / ROBOTIK 2010},
number = {Isr},
publisher = {VDE VERLAG GmbH},
address = {Munich, Germany},
abstract = {An internal representation of a scene is essential to generate actions on scene objects. A stabilized storage of object location and features offers the flexibility to process queries phrased in human-based terms relating to objects, which may not be in the current camera view. Scene representation is therefore an internal representation of the surrounding world that is stabilized against head and body movement. It contains associated information about location and features of objects. Because objects and bodies move, scene representation is not a one-time process, but a constantly scene- adapting mechanism of scanning for, storing, updating, and deleting information.
Our novel architecture incorporates the generation of autonomous scanning sequences on real-time camera images. The head can then be oriented towards a selected object and the color feature can be extracted. Object location and feature information are associatively stored in a three-dimensional Dynamic Neural Field. Changes in the scene, even for multiple objects, can be tracked simultaneously. The stored information is used to generate behavior for cued recall. Cues can be table regions, features, or object labels. The robot demonstrates a successful recall by centering its gaze on the stated object.},
keywords = {Autonomous robotics, dynamic neural field, Dynamical systems, man machine interaction, scene representation, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
An internal representation of a scene is essential to generate actions on scene objects. A stabilized storage of object location and features offers the flexibility to process queries phrased in human-based terms relating to objects, which may not be in the current camera view. Scene representation is therefore an internal representation of the surrounding world that is stabilized against head and body movement. It contains associated information about location and features of objects. Because objects and bodies move, scene representation is not a one-time process, but a constantly scene- adapting mechanism of scanning for, storing, updating, and deleting information.
Our novel architecture incorporates the generation of autonomous scanning sequences on real-time camera images. The head can then be oriented towards a selected object and the color feature can be extracted. Object location and feature information are associatively stored in a three-dimensional Dynamic Neural Field. Changes in the scene, even for multiple objects, can be tracked simultaneously. The stored information is used to generate behavior for cued recall. Cues can be table regions, features, or object labels. The robot demonstrates a successful recall by centering its gaze on the stated object.3.Zibner, Stephan K U; Faubel, Christian; Spencer, John P; Iossifidis, Ioannis; Schöner, Gregor
Scenes and Tracking with Dynamic Neural Fields: How to Update a Robotic Scene Representation Proceedings Article
In: Proc. Int. Conf. on Development and Learning (ICDL10), 2010.
BibTeX | Schlagwörter: Autonomous robotics, dynamic neural field, Dynamical systems, man machine interaction, scene representation, speech recognition
@inproceedings{Zibner2010c,
title = {Scenes and Tracking with Dynamic Neural Fields: How to Update a Robotic Scene Representation},
author = {Stephan K U Zibner and Christian Faubel and John P Spencer and Ioannis Iossifidis and Gregor Schöner},
year = {2010},
date = {2010-01-01},
booktitle = {Proc. Int. Conf. on Development and Learning (ICDL10)},
keywords = {Autonomous robotics, dynamic neural field, Dynamical systems, man machine interaction, scene representation, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
2.Zibner, Stephan K U; Faubel, Christian; Iossifidis, Ioannis; Schöner, Gregor
Scene Representation with Dynamic Neural Fields: An Example of Complex Cognitive Architectures Based on Dynamic Neural Field Theory Proceedings Article
In: Proc. Int. Conf. on Development and Learning (ICDL10), 2010.
BibTeX | Schlagwörter: Autonomous robotics, dynamic neural field, Dynamical systems, man machine interaction, scene representation, speech recognition
@inproceedings{Zibnersubmittedb,
title = {Scene Representation with Dynamic Neural Fields: An Example of Complex Cognitive Architectures Based on Dynamic Neural Field Theory},
author = {Stephan K U Zibner and Christian Faubel and Ioannis Iossifidis and Gregor Schöner},
year = {2010},
date = {2010-01-01},
booktitle = {Proc. Int. Conf. on Development and Learning (ICDL10)},
keywords = {Autonomous robotics, dynamic neural field, Dynamical systems, man machine interaction, scene representation, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
1.Zibner, Stephan; Faubel, Christian; Iossifidis, Ioannis; Schöner, Gregor; Spencer, John P
Scene and Tracking with Dynamic Neural Field Approach Proceedings Article
In: ISR / ROBOTIK 2010, Munich, Germany, 2010.
Abstract | BibTeX | Schlagwörter: Autonomous robotics, dynamic neural field, Dynamical systems, man machine interaction, scene representation, speech recognition
@inproceedings{Zibneri,
title = {Scene and Tracking with Dynamic Neural Field Approach},
author = {Stephan Zibner and Christian Faubel and Ioannis Iossifidis and Gregor Schöner and John P Spencer},
year = {2010},
date = {2010-01-01},
booktitle = {ISR / ROBOTIK 2010},
address = {Munich, Germany},
abstract = {An internal representation of a scene is essential to generate actions on scene objects. A stabilized storage of object location and features offers the flexibility to process queries phrased in human-based terms relating to objects, which may not be in the current camera view. Scene representation is therefore an internal representation of the surrounding world that is stabilized against head and body movement. It contains associated information about location and features of objects. Because objects and bodies move, scene representation is not a one-time process, but a constantly scene- adapting mechanism of scanning for, storing, updating, and deleting information.
Our novel architecture incorporates the generation of autonomous scanning sequences on real-time camera images. The head can then be oriented towards a selected object and the color feature can be extracted. Object location and feature information are associatively stored in a three-dimensional Dynamic Neural Field. Changes in the scene, even for multiple objects, can be tracked simultaneously. The stored information is used to generate behavior for cued recall. Cues can be table regions, features, or object labels. The robot demonstrates a successful recall by centering its gaze on the stated object.},
keywords = {Autonomous robotics, dynamic neural field, Dynamical systems, man machine interaction, scene representation, speech recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
An internal representation of a scene is essential to generate actions on scene objects. A stabilized storage of object location and features offers the flexibility to process queries phrased in human-based terms relating to objects, which may not be in the current camera view. Scene representation is therefore an internal representation of the surrounding world that is stabilized against head and body movement. It contains associated information about location and features of objects. Because objects and bodies move, scene representation is not a one-time process, but a constantly scene- adapting mechanism of scanning for, storing, updating, and deleting information.
Our novel architecture incorporates the generation of autonomous scanning sequences on real-time camera images. The head can then be oriented towards a selected object and the color feature can be extracted. Object location and feature information are associatively stored in a three-dimensional Dynamic Neural Field. Changes in the scene, even for multiple objects, can be tracked simultaneously. The stored information is used to generate behavior for cued recall. Cues can be table regions, features, or object labels. The robot demonstrates a successful recall by centering its gaze on the stated object.