Research fellow and PhD candidate at:

Karlsruhe Institute of Technology (KIT)
Campus Süd
Institute of Telematics
Chair for Pervasive Computing Systems / TECO, Building 07.07 Room 206
Vincenz-Prießnitz-Straße 1
76131 Karlsruhe
email: antonios@teco.edu


Robert Bosch GmbH
Research and Advance Engineering
Software Intensive Systems (CR/AEX3)
70465 Stuttgart

Tel. +49(711)811-49880
Mobil +49 172 4363011
email: Antonios.Karatzoglou@de.bosch.com


Research Interests:

  • Semantic-driven Optimization of Probabilistic and Machine Learning Models
  • Knowledge and Ontology engineering
  • Machine Learning and Probabilistic Modeling
  • Human-Computer-Interaction & Personalization
  • Semantic Enhanced Prediction


Short CV:

  • 2015 – (now): Research engineer and PhD Candidate
  • 2004 – 2014: Electrical Engineering and Information Technology (ETIT) Studies at the University of Karlsruhe (KIT),
    • Specialization topic: Automation and Information Processing (Control Engineering, Machine Learning, Robotics)
    • Diploma (aka Master) Thesis at Robert Bosch GmbH and the University of Karlsruhe (KIT), TECO
  • 1997-2003: Electrical and Electronic Engineering Studies at the University of Applied Sciences (TEIWM) in Kozani, Greece
    • PLC algorithm implementation


  • Diploma (aka Master) Thesis at Robert Bosch GmbH and TECO, KIT
  • Internship at Robert Bosch GmbH
  • Student Research Project at TECO, Karlsruhe Institute of Technlogy (KIT)
  • Student Research Assistant at TECO, Karlsruhe Institute of Technlogy (KIT)
  • Student Research Assistant at Fraunhofer Institute IOSB, Karlsruhe
  • Student Research Assistant at Engler-Bunte-Institute, Karlsruhe
  • Teaching Assistant at the Industrial Automation Laboratory at TEIWM, Kozani, Greece
  • Internship at KIKIS AVEE, Water Boilers and Solar Systems, Kozani, Greece



  • 2017 – (running): ScaleIT – Industry 4.0: Scalable ICT for increasing productivity in mechatronics manufacturing (ScaleIT)
  • 2016 – 2017: KEESMARTHOME – Comfort-Efficiency-Equilibrium in Smart Environments (Keesmarthome)



  • Chair at the 6th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2017)
  • Chair at the 11th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM 2017)


Supervised Bachelor/Master/PdF Theses:

  • Affective Semantic Trajectory Generator (Markus Szarvas)
  • Towards Building an Affective Synthetic Semantic Trajectory Generator: a User Study (Imanuel Richter)
  • Identification of environment- and context-specific key factors influencing the users’ thermal comfort through data mining (Yannick Meny)
  • Ontology-based Information Flow Control and Visualisation in an Industry 4.0 Scenario (Oliver König)
  • Activity-driven semantic similarity for semantic location prediction (Dominik Köhler)
  • Predictive Control for a comfort-efficiency-equilibrium in a Smart Home Environment (Vethiga Srikanthan, Julian Janßen)


Other Supervising Activities:

  • Mobile Computing and Ubiquitous Technologies Seminar
  • Designing and Conducting Experimental Studies Seminar
  • Praxis der Forschung (PdF)


Peer-reviewed Publications


Antonios Karatzoglou, Julian Janssen, Vethiga Srikanthan, Christof Urbaczek, Michael Beigl (2018) A Predictive Comfort- and Energy-aware MPC-driven Approach based on a Dynamic PMV Subjectification towards Personalization in an Indoor Climate Control Scenario, Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,, p. 89-100, SciTePress, doi:10.5220/0006702500890100


Antonios Karatzoglou, Michael Beigl (2017) Enhancing the Affective Sensitivity of Location Based Services Using Situation-Person-Dependent Semantic Similarity, The Eleventh International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM 2017) , USA Timothy Arndt, Cleveland State University, Japan Evgeny Pyshkin, University of Aizu, German Research Center for Artificial Intelligence Martin Ruskowski, Germany Martin.Ruskowski@dfki.de (DFKI), Federal Institute Farroupilha – Federal Aliane Loureiro Krassmann, Brazil University of Rio Grande do Sul, Japan Hiroaki Higaki, Tokyo Denki University (ed.), p. 95-100, Barcelona, Spain: IARIA

Antonios Karatzoglou, Stefan Lamp, Michael Beigl (2017) Matrix factorization on semantic trajectories for predicting future semantic locations, 2017 IEEE 13th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), p. 7, Rome, Italy: IEEE, url, doi:10.1109/WiMOB.2017.8115810

Antonios Karatzoglou, Michael Beigl (2017) [Poster] Applying Situation-Person-Driven Semantic Similarity On Location-Specific Cognitive Frames For Improving Location Prediction, 8th International Conference on Knowledge Engineering and Semantic Web (KESW 2017), p. 4-5, url, doi:10.13140/RG.2.2.15120.51209

Antonios Karatzoglou, Harun Sentürk, Adrian Jablonski, Michael Beigl (2017) Applying Artificial Neural Networks on Two-Layer Semantic Trajectories for Predicting the Next Semantic Location, Artificial Neural Networks and Machine Learning -- ICANN 2017: 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14, 2017, Proceedings, Part II, Alessandra Lintas, Stefano Rovetta, Paul F M J Verschure, Alessandro E P Villa (ed.), p. 233-241, Cham: Springer International Publishing, url, doi:10.1007/978-3-319-68612-7_27

A. Karatzoglou, J. Janßen, V. Srikanthan, Y. Ding, M. Beigl (2017) Comfort-efficiency-equilibrium a proactive, at room level individualized climate control system for smart buildings, SMARTGREENS 2017 - Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems


Antonios Karatzoglou, Markus Scholz, Till Riedel, Michael Beigl (2012) A prototype of an in-situ radio sensing and visualization device. Demo Paper., Proceedings of Ninth International Conference on Networked Sensing Systems (INSS 2012), Antwerp, Belgium: IEEE


  • A Method and System for Identifying at Least One (Contextual) State of a Certain Space or Room (Verfahren und System zum Erfassen mindestens eines Zustands eines Raums.)