• Intelligente Sensorsysteme (Bachelor)
• Rechnerstrukturen und Maschinennahes Programmieren (Bachelor)
• Betriebssysteme (Bachelor)
• Autonomes Fahren und Robotik (Master)
• WP Einführung in die Robotik (Bachelor)
• WP Einführung in Computer Engineering (Bachelor)
• Algorithmen und Datenstrukturen (Bachelor)
• Master-Grund-/-Hauptseminare, Bachelor-Seminare
• Projekte: Lehr-CPU-/Lehr-BS-Entwicklung, Deep Learning, Autonome Systeme

• Intelligente Sensorik
• Sensordatenverarbeitung
• Maschinelle Lernverfahren (ML)
• Miniaturautonomie
• autonome Systeme, Robotik, Unterwasserrobotik
• Anwendungen im Verkehrs-/Automotive-Kontext, autonomes Fahren

• Mitglied in der Forschungsgruppe AutoSys :  autosys.informatik.haw-hamburg.de/authors/timtiedemann/
• Stellvertretendes Mitglied im Hochschulsenat
• Stellvertretendes Mitglied im Fakultätsrat TI
• Mitglied Ethikkommission der HAW
• Fellow des Stifterverbandes (2018)
• IEEE

• Deep Learning for Time Series Classification and Prediction on Big Crowd Sensed Automotive Data (Master-Arbeit)
• Bachelor-/Master-Arbeiten zu FPGA-basierter Implementierung maschineller Lernverfahren oder anderer spezifischer Algorithmen
• Bachelor-/Master-Arbeiten zu datengetriebener Sensordatenfusion
• Bachelor-/Master-Arbeiten zur Sensordatenverarbeitung in verschiedenen Anwendungen der Robotik
• Master-Arbeiten/Master-Projekte in den Bereichen Kooperation im autonomen Fahren, Miniaturautonomie, autonome Systeme, Robotik

[Publikationsliste (vor 2016) noch im Aufbau]

2022:

• Tiedemann, T., Schwalb, L., Kasten, M., Grotkasten, R., & Pareigis, S. (2022). Miniature autonomy as means to find new approaches in reliable autonomous driving AI method design. Frontiers in Neurorobotics, Vol. 16, ISSN 1662-5218, URL: www.frontiersin.org/articles/10.3389/fnbot.2022.846355, doi:https://doi.org/10.3389/fnbot.2022.846355

• Tiedemann, T.; Cordes, F.; Keppner, M. and Peters, H. (2022). Challenges of Autonomous In-field Fruit Harvesting and Concept of a Robotic Solution.  In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics, ISBN 978-989-758-585-2, ISSN 2184-2809, pages 508-515.
• Tiedemann, T. and Thill, S. (ass. eds., chairs): The BROAD Workshop. WS at the 33rd IEEE Intelligent Vehicles Conference, June 2022, Aachen.
• Tiedemann, T.: Of Miniature Autonomy on Land, on Water and in the Air and Why IV Should Bother about It. Talk at the joined BROAD/RVT WS at the 33rd IEEE Intelligent Vehicles Conference, June 2022, Aachen.

2021: 

• Juri Zach, Christian Busse, Steffen Funk, Christian Möllmann, Bernd-Christian Renner, and Tim Tiedemann: Towards Non-invasive Fish Monitoring in Hard-to-Access Habitats
  Using Autonomous Underwater Vehicles and Machine Learning. Proceedings of the OCEANS 2021 (accepted and presented)
• Tim Tiedemann, Matthis Keppner, Tom Runge, Thomas Vögele, Martin Wittmaier and Sebastian Wolff: Concept of a Robotic System For Autonomous Coarse Waste Recycling. Proceedings of the ICINCO 2021. SCITEPRESS. (accepted and presented)
• Tiedemann, T. and Anderson, S. (ass. eds., chairs): The BROAD Workshop. WS at the 32nd IEEE Intelligent Vehicles Conference, July 2021, Nagoya. 

2020:

• Pareigis, S., Tiedemann, T., Kasten, M., Stehr, M., Schnirpel, T., Schwalb, L. and Burau, H., 2021. Künstliche Intelligenz in der Miniaturautonomie. In Echtzeit 2020 (pp. 41-50). Springer Vieweg, Wiesbaden.
• Tiedemann, T. and Anderson, S. (ass. eds., chairs): The BROAD Workshop. WS at the 31st IEEE Intelligent Vehicles Conference, November 2020, Las Vegas. 

2019:

• Tiedemann, T. (2019): Adversarial Attacks: How To Fool An Artificial Neural Network? Invited talk at the tell-me days 2019, 26.-28.06.2019. HAW Hamburg. URL: youtu.be/-ZZgws-8sXA
• Tim Tiedemann, Jonas Fuhrmann, Sebastian Paulsen, Thorben Schnirpel, Nils Schönherr, Bettina Buth, and Stephan Pareigis (2019): Miniature Autonomy as One Important Testing Means in the Development of Machine Learning Methods for Autonomous Driving: How ML-Based Autonomous Driving Could Be Realized on a 1:87 Scale. In Proceedings of the ICINCO 2019. SCITEPRESS. (accepted)
• Stephan Pareigis, Tim Tiedemann, Jonas Fuhrmann, Sebastian Paulsen, Thorben Schnirpel, Nils Schönherr. Miniaturautonomie und Echtzeitsysteme. In Tagungsband Echtzeit 2019, Springer Lecture Notes. Springer. (accepted)
• Tim Tiedemann, Jonas Fuhrmann, Sebastian Paulsen, Thorben Schnirpel, Bettina Buth, and Stephan Pareigis (2019): Miniature Autonomy as Testing Platform to Tackle Challenges of Autonomous Driving. Poster (not peer-reviewed!) at the IEEE Intelligent Vehicles Conference 2019. June 2019, Paris.
• Tiedemann, T. and Anderson, S. (ass. eds., chairs): The BROAD Workshop. WS at the 30th IEEE Intelligent Vehicles Conference, June 2019, Paris. 
• Markus Linke und Tim Tiedemann: "Individuelle online Lernwege in der Technischen Mechanik mit Maschinellen Lernverfahren" Posterbeitrag zum Fellowtreffen des Programms Innovationen in der Hochschullehre, 15 März 2019, Deutscher Stifterverband für die Deutsche Wissenschaft

2018:

• Tiedemann, T. (2018): Communication Hardware, in: Bosse, S., Lehmhus, D., Lang, W. and Busse, M. (2018)  Material-Integrated Intelligent Systems - Technology and Applications: Technology and Applications, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. doi: 10.1002/9783527679249.ch15

2017:

• Schenck, Horst, Tiedemann, Gaulik, Möller (2017): Comparing parallel hardware architectures for visually guided robot navigation. Concurrency Computat.: Pract. Exper., 29: pe3833, doi: 10.1002/cpe.3833
• Tiedemann, Bauer, Kirchner: Concept of Cognitively Inspired Automotive Sensor Data Fusion. Talk at IEEE Intelligent Vehicles 2017, WS on Cognitively Inspired Vehicles.
• Tiedemann, Backe, Vögele, Conradi: Automotive Ad Hoc Sensor Networks in the Project SADA: Concept and Current State. Poster presentation at the "Fachgespräche Sensornetze 2017".
• Tiedemann: Dynamic and Automatic Sensor Data Fusion in the Automotive Research Project SADA. Talk at the Int. Conf "Vehicle Intelligence", Dec. 2017, Munich. 

2016:

• Tim Tiedemann, Christian Backe, Thomas Vögele, Peter Conradi (2016): An Automotive Distributed Mobile Sensor Data Collection with Machine Learning Based Data Fusion and Analysis on a Central Backend System. Procedia Technology, Volume 26, 2016, Pages 570-579, ISSN 2212-0173, dx.doi.org/10.1016/j.protcy.2016.08.071.
• Wendelin Feiten, Susana Alcalde Baguees, Michael Fiegert, Feihu Zhang, Dhiraj Gulati, Tim Tiedemann: A New Concept for a Cooperative Fusion Platform. Proceedings of 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.
• Susana Alcalde Bagüés, Wendelin Feiten, Tim Tiedemann, Christian Backe, Dhiraj Gulati, Steffen Lorenz and Peter Conradi: Towards Dynamic and Flexible Sensor Fusion for Automotive Applications. Proceedings of the 20th International Forum on Advanced Microsystems for Automotive Applications (AMAA 2016).

2015:

• T. Tiedemann, T. Vögele, Mario M. Krell, Jan H. Metzen, F. Kirchner: Concept
  of a Data Thread Based Parking Space Occupancy Prediction in a Berlin Pilot
  Region. Proceedings of the AAAI Workshop on AI for Transportation (WAIT),
  2015.
• T. Köhler: Bio-Inspired Motion Detection Based on an FPGA Platform. In G.
  Cristobal et al. (Herausgeber): Biologically-Inspired Computer Vision:
  Fundamentals and Applications, Wiley-VCH, Weinheim, Kapitel 17, Okt/2015.
  ISBN: 978-3-527-41264-8. (Buchkapitel)
• T. Tiedemann, T. Vögele: Wissen, wann ein Parkplatz frei wird. In
  Internationales Verkehrswesen, DVV Media Group GmbH, volume 67, pages
  84-85, 2015. (nicht peer-reviewed)

2014:

• Tim Köhler, Elmar Berghöfer, Christian Rauch, Frank Kirchner: Sensor Fault Detection and Compensation in Lunar/Planetary Robot Missions Using Time-Series Prediction Based on Machine Learning. In Acta Futura, ESA Advanced Concepts Team, ESTEC, volume Issue 9: AI in Space Workshop at IJCAI 2013, pages 9-20, May/2014.

2013:

• Christian Rauch, Elmar Berghöfer, Tim Köhler, Frank Kirchner: Comparison of Sensor-Feedback Prediction Methods for Robust Behavior Execution. In KI 2013: From Research to Innovation and Practical Applications, (KI-13), 16.9.-20.9.2013, Koblenz, Springer, pages 200-211, Sep/2013. ISBN: 978-3-642-40941-7.
• Elmar Berghöfer, Denis Schulze, Christian Rauch, Marko Tscherepanow, Tim Köhler, Sven Wachsmuth: ART-based fusion of multi-modal perception for robots. In Neurocomputing, Elsevier, volume 107, pages 11-22, May/2013.

2012:

• Tim Köhler, Christian Rauch, Martin Schröer, Elmar Berghöfer, Frank Kirchner: Concept of a Biologically Inspired Robust Behaviour Control System. In Proceedings of International Conference on Intelligent Robotics and Applications 2012, (ICIRA-12), 03.10.-05.10.2012, Montreal, Québec, Springer Berlin / Heidelberg, pages 486-495, Oct/2012. ISBN: 978-3-642-33514-3.
• Christian Rauch, Tim Köhler, Martin Schröer, Elmar Berghöfer, Frank Kirchner: A Concept of a Reliable Three-Layer Behaviour Control System for Cooperative Autonomous Robots. In Proceedings of the German Conference on Artificial Intelligence, (KI-2012), 24.9.-27.9.2012, Saarbrücken, o.A., Sep/2012.

2009:

• Köhler, T., Röchter, F., Lindemann, J. P., & Möller, R.: Bio-inspired motion detection in an FPGA-based smart camera module. Bioinspiration & biomimetics, 4(1), 015008.