Markus Amann received a M.Sc. degree in Electrical Engineering from Technical University Darmstadt, Germany.
He works as Scientist at Honda Research Institute Europe GmbH, Germany, and pursues a Ph.D. at the University of Alcalá (Alcalá de Henares, Madrid), Spain, in the field of Advanced Driver Assistance and Vehicle Automation.

His research interests include pedestrian-vehicle interactions, behavior modeling, and behavior planning for automated systems.

Amann, M., Probst, M., Wenzel, R., and Weisswange, T. H. (2024). Enabling cooperative pedestrian-vehicle interactions using an ehmi. In 35th IEEE Intelligent Vehicles Symposium (IV), pages 2962–2969. IEEE. 10.1109/IV55156.2024.10588537.

Amann, M., Probst, M., Wenzel, R., Weisswange, T. H., and Sotelo, M. ´A. (2025). Optimal behavior planning for implicit communication using a probabilistic vehicle-pedestrian interaction model.

In 36th IEEE Intelligent Vehicles Symposium (IV), pages 1156–1163. IEEE. 10.1109/IV64158.2025.11097656.

Melo Castillo, A. N., Amann, M., Salinas Maldonado, C., Aramrattana, M., Weisswange, T. H., Probst, M., and Sotelo, M. ´A.19 (2025). Towards incorporating pedestrian intention predictions into behavior planning using virtual reality co-simulators. In 36th IEEE Intelligent Vehicles Symposium (IV), pages 2571–2576. IEEE. 10.1109/IV64158.2025.11097738.

2013 BSc.: Psychology, Chemnitz University of Technology, Germany
2016 MSc.: Psychology, Chemnitz University of Technology, Germany
2023 PhD: Psychology, Chemnitz University of Technology, Germany (“Activity trackers in everyday life: Characteristics of usage motivation and user diversity to explain individual usage trajectories”)
2023-2025: PostDoc Psychology, University of Lübeck, Germany
2025-2026: Guest Scientist, Honda Research Institute Europe

Human-AI Interaction
User Diversity
Psychological Basic Needs

C. Attig, L. Winzer, T. Schrills, M. Zoubir, M. Mortaga, P. Wollstadt, C. Wiebel-Herboth, and T. Franke, “Understanding successful human–AI teaming: The role of goal alignment and AI autonomy for social perception of LLM-based chatbots”, Comp. Hum. Behav: Artif. Hum., vol. 7, 100246, 2026.

C. Attig, P. Wollstadt, T. Schrills, T. Franke, and C. Wiebel-Herboth, “More than task performance: Developing new criteria for successful human-AI teaming using the cooperative card game Hanabi”, in Ext. Abstr. CHI Conf. Hum. Factors Comput. Syst. (CHI EA ’24), New York, NY, USA: ACM, 2024.

T. Franke, C. Attig, and D. Wessel, “A personal resource for technology interaction: Development and validation of the Affinity for Technology Interaction (ATI) scale”, Int. J. Hum.-Comput. Interact., vol. 35, pp. 456–467, 2019.

2005 M.Sc.: Computer Engineering, Sapienza University Rome
2009 PhD: Computer Engineering, Sapienza University Rome
2019 Habilitation in Cognitive Science: Eberhard Karls University of Tübingen

Eye-hand coordination in object interaction
Sensorimotor aspects of visual perception
Computational models of visual attention

Belardinelli, A., Lohmann, J., Farnè, A., & Butz, M. V. (2018). Mental space maps into the future. Cognition, Vol. 176, July 2018, 65-73.

A. Belardinelli, M. Stepper and M.V. Butz (2016). It’s in the Eyes: Planning Precise Manual Actions Before Execution. Journal of Vision, Vol.16, 18.

M. Wischnewski, A. Belardinelli, W.X. Schneider and J.J. Steil (2010). Where to Look Next? Combining Static and Dynamic Proto-objects in a TVA-based Model of Visual Attention. Cognitive Computation, 2 (4) , p. 326 – 343

2000 Physics degree from Wuppertal University
2000-2005 PhD research at Bochum University
Since 2005 at Honda Research Institute

Intelligent artificial systems
Systems research
Perception subsystems

B. Bolder, H. Brandl, M. Heracles, H. Janßen, I. Mikhailova, J. Schmüdderich and C. Goerick, “Expectation-driven Autonomous Learning and Interaction System”, in IEEE-RAS Int. Conf. on Humanoid Robotics, 2008.

Q. Li, M. Meier, R. Haschke, H. Ritter and B. Bolder, “Object Dexterous Manipulation in Hand Based on Finite State Machine”, Int. J. of Mechatronics and Automation, pp. 1185-1190, 2012.

T. Weisswange, B. Bolder, J. Fritsch, S. Hasler and C. Goerick, “An Integrated ADAS for Assessing Risky Situations in Urban Driving”, in Proc. IEEE Intell. Veh. Symp. (IV 2013), pp. 292-297.

2014 B.Sc.: Physics, Universitat de Valencia.
2018 M.Sc.: Theoretical Physics (Cum Laude), Universiteit Leiden.
2022 PhD: Theoretical Physics, Universiteit Leiden.
2022-2025 Postdoc: NWO grant, Quantum Software Consortium, Universiteit Leiden.

Quantum Computing and Algorithms
Quantum Sensing
Quantum-enhanced Machine Learning
Machine-learning for Quantum Physics

Hierarchically discriminating Haar-randomness in quantum states from a black-box device (pub-6605), New Journal of Physics.

The role of data-induced randomness in quantum machine learning classification tasks (pub-6475), NPJ Quantum Information.

Experimental demonstration of the absence of noise-induced barren plateaus using information content landscape analysis (pub-6244).

2012: Visiting student, Indian Institute of Technology Madras, India
2016 M.Sc.: Mechanical and Process Engineering, Technische Universität Darmstadt, Germany
2020 PhD: Dr.-Ing., Institute for Fluid Mechanics and Aerodynamics, Technische Universität Darmstadt, Germany (“Hydrodynamic investigations of rapidly stretched liquid bridges”, DFG CRC 1194)
Since 2021: Senior Scientist, Honda Research Institute Europe, Offenbach, Germany

Cooperative Programmable Matter
Nonlinear Dynamics and Complex Physical Systems
Transport Systems and Agent-based Simulations

S. Brulin and E. Estrada, “Topology-Guided Probe Placement for Screening Critical Road Links in Urban Mobility Networks”, NetSci 2026, Boston, USA, 2026.

N. Hohmann, S. Brulin, J. Adamy and M. Olhofer, “Multi-objective optimization of urban air transportation networks under social considerations”, IEEE Open Journal of Intelligent Transportation Systems, vol. 5, pp. 589-602, 2024.

P. Brockmann, M. Lannert, H. Ennayar, Y. Cao, X. Dong, Z. Zhang, S. Brulin et al., “Enhancement of interfacial instabilities by solid particles during fast stretching of a liquid suspension bridge”, Soft Matter, vol. 21, pp. 6559-6574, 2025.

2025 M.Sc.: Automation Engineering, Politecnico di Bari, Italy

Machine Learning
Deep Learning
Systems Optimization

2016 BSc.: Computer Science, Universidade Federal de Mato Grosso do Sul, Brazil
2020 MSc.: Computer Science, Universidade Federal do Rio Grande do Sul, Brazil
2026 PhD: Computer Science, University of Stuttgart, Germany (“Expanding the Boundaries of Augmented Reality Evaluations: 3 Case Studies with Increased Complexity”)

Human-Computer Interaction
Human-Robot Interaction
Augmented and Virtual Reality
Interdisciplinary Research

Aimée Sousa Calepso, Anna Belardinelli, Valerie Behrwind, Christine Knoll, Sarah B. Blakeslee, Bernhard Sendhoff , “Toward automated interactions for pediatric inpatients using a social robot”, 21st ACM/IEEE International Conference on Human-Robot Interaction (HRI 2026): Workshop on Robots for Care, 2026.

Christine Knoll, Niklas Giesa, Sarah B. Blakeslee, Valerie Behrwind, Aimée Sousa Calepso, Anna Belardinelli, Felix Balzer, Katarina Braune , “Beyond the Circuit. Evaluating the Impact and Integration of Outcomes of Social Robots in Pediatric Healthcare: A Systematic Review. “, MEDINFO 2025, 1970.

2019 M.Sc.: Electronic Engineering, Universita’ Politecnica delle Marche, Ancona, Italy

Machine Learning
Deep Learning
Time-Series Analysis

Andrea Castellani, Sebastian Schmitt, and Stefano Squartini, “Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning,” IEEE Transactions on Industrial Informatics, vol. 17, no. 7, pp. 4733-4742, July 2021.

Andrea Castellani, Sebastian Schmitt, and Barbara Hammer, “Task-Sensitive Concept Drift Detector with Constraint Embedding,” 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando FL, USA, pp. 01-08, December 2021.

Andrea Castellani, Sebastian Schmitt, and Barbara Hammer, “Stream-Based Active Learning with Verification Latency in Non-stationary Environments,” Artificial Neural Networks and Machine Learning – ICANN 2022. Lecture Notes in Computer Science, vol. 13532, pp. 260-272, September 2022.

Diploma: Computer Science, University Pisa, Italy
MBA: University of Warwick, United Kingdom

Software Infrastructures and Integration Middlewares
Intelligent and Modular Systems
Multi-Agent AI-Based Infrastructures

Ceravola, Antonello, Frank Joublin, Ahmed R. Sadik, Bram Bolder, and Juha-Pekka Tolvanen. “HyperGraphOS: A Meta Operating System for Science and Engineering.” arXiv preprint arXiv:2412.04923 (2024).

Ebubechukwu Ike, Johane Takeuchi, Antonello Ceravola, Frank Joublin , “Automating Dialogue Evaluation: Large Language Mode versus Human Judgement”, HCI International 2025, 2025.

Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Jörg Deigmöller, Anna Belardinelli, Chao Wang, Daniel Tanneberg, Stephan Hasler, Michael Gienger , “CoPAL: Corrective Planning of Robot Actions with Large Language Models”, International Conference on Robotics and Automation (ICRA), 2024.

2023 M.Sc.: High Integrity Systems, Frankfurt University of Applied Sciences

Software Development
Machine Learning Algorithms
Autonomous Driving

Diploma: Rhein-Main University of Applied Science, Germany
PhD: Brunel University London, United Kingdom

Knowledge Representation and reasoning
Computer Vision
Machine Learning

J. Eggert, J. Deigmoeller, L. Fischer, A. Richter, “Memory Nets: A Knowledge Representation for Autonomous Entities”, 11th International Conference on Knowledge Engineering and Knowledge Ontology Development, 2019.

L. Fischer, S. Hasler, J. Deigmoeller, T. Schnuerer, M. Redert, U. Pluntke, K. Nagel, C. Senzel, J. Ploennigs, A. Richter, J. Eggert, “Which Tool to Use? Grounded Reasoning in Everyday Environments with Assistant Robots”, Proceedings of the 11th Cognitive Robotics Workshop, 2018.

J. Deigmöller, N. Einecke, O. Fuchs, and H. Janssen, “Road surface scanning using stereo cameras for motorcycles”, 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2018, pp. 549–554.

2010 Diploma: Mechatronics, University of Applied Science, Nuernberg, Germany
2013 M.A.: Philosophy of Technology, Technische Universität Darmstadt, Germany
2018 PhD: Technische Universität Darmstadt, Germany

Ethics of Artificial Intelligence
Human-Machine Interaction
Philosophy of Technology

M. Dietrich, T.H. Weisswange, “Distributive Justice as an Ethical Principle for Autonomous Vehicle Behavior Beyond Hazard Scenarios”, Ethics and Information Technology, Berlin: Springer, 2019, pp. 1 – 13.

M. Dietrich, E. Berlin and K. van Laerhoven, “Assessing Activity Recognition Feedback in Long-term Psychology Trials”, Proc. of the Int. Conf. on Mobile and Ubiquitous Multimedia (MUM), ACM, Linz, Austria, 2015, pp. 121-130.

M. Dietrich and K. van Laerhoven, “Reflect Yourself! Opportunities and Limits of Wearable Activity Recognition for Self-Tracking”, Lifelogging: Digital self-tracking and Lifelogging – between disruptive technology and cultural transformation, S. Selke, Ed. Wiesbaden: Springer Fachmedien, 2015, pp. 213-233.

I am a Senior Chief Scientist at Honda Research Institute Europe and lead the Knowledge & Cognition competence group.
My research focuses on cognitive agents that support humans through common-sense reasoning, knowledge representation, life-long memory, and risk-aware decision making.

My work combines probabilistic models, knowledge graphs, and agentic AI architectures.
I am particularly interested in building systems that can reason under uncertainty, integrate knowledge with learning, and continuously adapt from experience.

A Grounded Memory System For Smart Personal Assistants (2025)
Memory Net: Generalizable Common-Sense Reasoning over Real-World Actions and Objects (2023)
Introducing Risk Shadowing for Decisive and Comfortable Behavior Planning (2023)
Automated Driving in Complex Real-World Scenarios using a Scalable Risk-Based Behavior – Generation Framework (2021)
Predictive risk estimation for intelligent ADAS functions (2014)

2006 Diploma: Technical University Ilmenau, Germany
2012 PhD: Technical University Ilmenau, Germany
2023:Head of Reliable Systems and Software group

Computer Vision
Optimization
Efficient Algorithms
Outdoor Robotics

C. Bergmeir, F. De Nijs, E. Genov, A. Sriramulu, M. Abolghasemi, R. Bean, …, N. Einecke, …, and R. Yuan, “Predict+ Optimize Problem in Renewable Energy Scheduling”, IEEE Access, 2025, 13, pp. 60064-60087.

T. Uriot, D. Izzo, L.F. Simões, R. Abay, N. Einecke, S. Rebhan, …, and K. Merz, “Spacecraft collision avoidance challenge: Design and results of a machine learning competition”, Astrodynamics, 2022, 6(2), pp. 121-140.

M. Franzius, M. Dunn, N. Einecke, and R. Dirnberger, “Embedded robust visual obstacle detection on autonomous lawn mowers”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 44-52.

N. Einecke and J. Eggert, “A multi-block-matching approach for stereo”, Intelligent Vehicles Symposium, Seoul, 2015, pp. 585-592.

2018: M.Sc. Mechatronik, TU Darmstadt, Specialization: Simulation and Control of Mechatronic Systems

Software Quality
Technology Modernization
Agile Engineering Practices

Living Lab: A 24/7 Human-Machine-Interaction Space in an Office Environment (2019)

SmartLobby: Using a 24/7 Remote Head-Eye-Tracking for Content Personalization (2019)

2016 MSc: Mechatronics, Technical University of Darmstadt, Germany
2026 PhD: Technical University of Darmstadt, Germany (“Relational Local Dynamic Maps for Advanced Driver Assistance Systems”)

Digital Cartography
Social Simulation
Neuro-Symbolic AI
Vision-Based Localization

B. Flade, A. Koppert, G. Velez, A. Das, D. Betaille, G. Dubbelman, O. Otaegui and J. Eggert, “Vision-Enhanced Low-Cost Localization in Crowdsourced Maps”, IEEE Intell. Transp. Syst. Mag., vol. 12, no. 3, 2020, pp. 70-80.

B. Flade, S. Kohaut, D.S. Dhami, J. Eggert and K. Kersting, “StaR Maps: Unveiling Uncertainty in Geospatial Relations”, Proc. IEEE 27th Int. Conf. Intell. Transp. Syst. (ITSC), 2024, pp. 497-504.

S. Kohaut, B. Flade, D. Ochs, D. S. Dhami, J. Eggert and K. Kersting, “Probabilistic Mission Design for Neuro-Symbolic Unmanned Aircraft Systems”, IEEE Trans. Intell. Transp. Syst., vol. 26, no. 12, 2025, pp. 22751-22760.

Diploma: Computer Science, Brandenburg Technical University Cottbus, Germany
PhD: Dr.rer.nat., (Theoretical Biology), Humboldt University, Berlin, Germany

Outdoor robotics
Unsupervised Learning
Self-Localization

B. Metka, M. Franzius, U. Bauer-Wersing, “Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis”. PLoS ONE 13(9): e0203994. https://doi.org/10.1371/journal.pone.0203994.

M. Haris, M. Franzius, U. Bauer-Wersing, “Robot Navigation on Slow Feature Gradients”. International Conference on Neural Information Processing (pp. 143-154) 2018.

B. Metka, M. Franzius, U. Bauer-Wersing, “Efficient navigation using slow feature gradients”. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1311-1316) 2017.

2025 M.Sc. Eng.: General Engineering with specialisation in Mechatronics, École d’Ingénieurs en Génie des Systèmes Industriels (EIGSI – La Rochelle), La Rochelle, France

Generative AI for Engineering Design
Evolutionary Optimization & Geometry Processing
Computer Science & Machine Learning

2016: M. Eng. Information Technology, SRH University Heidelberg, Germany

Software Infrastructure and Integration
System Design and Architecture

Diploma: Electronics and computer science, EUDIL, Lille, France
PhD: Neuroscience, University of Rouen, France
Post-doc, Ruhr-University Bochum, Germany
Phillips Speech Processing, Aachen, Germany

Semantic acquisition
Developmental robotics
Auditory signal processing in the brain
LLM-based Multi-Agent Architecture

Geuenich, J., Joublin, F., Ceravola, A., Brandenburg, S., & Dodge, J. (2026). AI Can See What You Can’t See: How LLM-Agents Complement Human-Based Gender-Inclusive Usability Testing. ACM Transactions on Interactive Intelligent Systems, 16(2), 1-34.

Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Jörg Deigmöller, Anna Belardinelli, Chao Wang, Daniel Tanneberg, Stephan Hasler, Michael Gienger , “CoPAL: Corrective Planning of Robot Actions with Large Language Models”, International Conference on Robotics and Automation (ICRA), 2024.

Antonello Ceravola, Frank Joublin, Ahmed Sadik, Bram Bolder, Juha-Pekka Tolvanen , “HyperGraphOS: A Meta Operating System for Science and Engineering”, MODELSWARD 2025, issue Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering (MODELSWARD 2025), pp. 64-74, 2024.

Antonello Ceravola, Frank Joublin, Johane Takeuchi, Ebubechukwu Ike , “Catalyzing Creativity Empowering Collaborative Brainstorming With Multifaceted Virtual Agents”, Artificial Intelligence & Intelligent Automation 2024 (IAAI), 2024.

Augmented Perception
Cooperative Human-Machine Interaction
Adaptive Interfaces

M. Krüger, C. Wiebel and H. Wersing (2017). From Tools Towards Cooperative Assistants. 5th International Conference on Human Agent Interaction, pp. 287-294.

Krüger, M., Wiebel-Herboth, C. B., & Wersing, H. (2020). The Lateral Line: Augmenting Spatiotemporal Perception with a Tactile Interface. In Proceedings of the Augmented Humans International Conference, pp. 1-10.

Krüger, M., Wiebel-Herboth, C. B., & Wersing, H. (2021). Tactile encoding of directions and temporal distances to safety hazards supports drivers in overtaking and intersection scenarios. Transportation Research Part F: Traffic Psychology and Behaviour, 81, pp. 201-222.

2018 MSc.: Mechatronics, Technische Universität Darmstadt, Germany
2023 Ph.D.: Computer Science, Universität Bielefeld, Germany

Artificial Intelligence for Engineering
Computational Creativity
Human Factors Research

Limmer et al, Design of fair and interpretable electric vehicle charging policies through genetic programming, Applied Energy, 2026, https://doi.org/10.1016/j.apenergy.2025.127176

Engel et al, A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning, Nature Scientific Data, 2025, https://doi.org/10.1038/s41597-025-05186-3

Lanfermann et al, Identification of energy management configuration concepts from a set of pareto-optimal solutions, Energy Conversion and Management: X, 2024, https://doi.org/10.1016/j.ecmx.2024.100576

Steffen Limmer received the M.Sc. (Diploma) in computer science from the University of Jena, Germany in 2009.
In 2016, he received the Ph.D. degree in engineering from the University of Erlangen-Nürnberg, Germany, where he worked from 2009 to 2016 as scientific assistant at the chair of computer architecture.
Since December 2016, Dr. Limmer is senior scientist at the Honda Research Institute Europe.

Numerical optimization
Energy Management
Machine Learning

Baratchi, M., Wang, C., Limmer, S., Van Rijn, J. N., Hoos, H., Bäck, T., & Olhofer, M. (2024). Automated machine learning: past, present and future. Artificial intelligence review, 57(5), 122.

Soares, J., Lezama, F., Faia, R., Limmer, S., Dietrich, M., Rodemann, T., Ramos, S., & Vale, Z. (2024). Review on fairness in local energy systems. Applied Energy, 374, 123933.

Limmer, S., Kenny, A., Ray, T., Lanfermann, F., Singh, H. K., & Castellani, A. (2026). Design of fair and interpretable electric vehicle charging policies through genetic programming. Applied Energy, 404, 127176.

2014: M.Sc. in Information System Technology, Technische Universität Darmstadt, Germany
2017: Dr.-Ing. (Ph.D.), Intelligent Autonomous Systems Lab (Supervisor: Prof. Jan Peters), Technische Universität Darmstadt, Germany

Machine Learning
Robotics
Behavior Generation

S. Manschitz, B. Gueler, W. Ma and D. Ruiken, “Sampling-Based Grasp and Collision Prediction for Assisted Teleoperation,” 2025 IEEE

International Conference on Robotics and Automation (ICRA), Atlanta, GA, USA, 2025, pp. 8174-8180, doi: 10.1109/ICRA55743.2025.11128199.

S. Manschitz, M. Gienger, J. Kober, J. Peters, “Mixture of Attractors: A novel Movement Primitive Representation for Learning Motor Skills from Demonstrations”, IEEE Robotics and Automation Letters (RA-L), vol. 3, num. 2, pp. 926-933, 2018.

S. Manschitz, J. Kober, M. Gienger, J. Peters, “Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations”, Robotics and Autonomous Systems, vol. 74, pp. 97-107, 2015.

2014 Diploma (M.Sc. equiv.), Computer Science, Lomonosov Moscow State University, Russia
2022 Ph.D., Dr.-Ing., Technical University of Darmstadt, Germany

Artificial Intelligence for cybersecurity
Threat intelligence
Privacy-enhancing technologies
AI engineering

A survey on privacy-preserving computing in the automotive domain N Yuca, N Matyunin, E Arzoglou, NA Anagnostopoulos, S KatzenbeisserACM Computing Surveys (CSUR) 58 (5), 1-36

Uncovering CWE-CVE-CPE relations with threat knowledge graphs Z Shi, N Matyunin, K Graffi, D Starobinski ACM Transactions on Privacy and Security (TOPS) 27 (1), 1-26

TargetFuzz: Using DARTs to Guide Directed Greybox Fuzzers S Canakci, N Matyunin, K Graffi, A Joshi, M Egele Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security (AsiaCCS), 2022

Threat modeling tools: A taxonomy Z Shi, K Graffi, D Starobinski, N Matyunin IEEE Security & Privacy 20 (4), 29-39

1998 Diploma: Civil Engineering, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Germany
1998-1999: Philipp Holzmann AG, Düsseldorf, Germany
2004 PhD: Dr.-Ing., Technische Universität Darmstadt, Germany

Generative Artificial Intelligence
Evolutionary Optimization
Geometry Processing

M. Wong, Y. Lyu, T. Rios, S. Menzel , Y.S. Ong, “LLM-to-Phy3D: Physically Conform Online 3D Object Generation with LLMs”, IEEE Congress on Evolutionary Computation, 2026

T. Rios, F. Lanfermann, S. Menzel, “Large Language Model-assisted Surrogate Modelling for Engineering Optimization”, IEEE Conference on Artificial Intelligence, 2024

R. Canaan, X. Gao, J. Togelius, A. Nealen, S. Menzel, “Generating and Adapting to Diverse Ad-Hoc Partners in Hanabi”, IEEE Transactions on Games, 2022

2008 Diploma: Computer Science, Technical University Ilmenau, Germany
2011 Ph.D. (Dr.-Ing.): Robotics, Bielefeld University, Germany (“A Whole Systems Approach to Robot Imitation Learning of Object”)

Robotic Systems and Integration
Software Engineering

Mühlig, A. Hayashi, M. Gienger, S. Iba and T. Yoshiike, “Receding Horizon Optimization of Robot Motions generated by Hierarchical Movement Primitives”, Proc. IEEE Int. Conf. on Intell. Robot. Syst. (IROS), 2014, pp. 129-135.

A.-L. Vollmer, M. Mühlig, J. J. Steil, K. Pitsch, J. Fritsch, K. J. Rohlfing and B. Wrede, “Robots Show Us How to Teach Them: Feedback from Robots Shapes Tutoring Behavior during Action Learning”, PLoS ONE, vol. 9, no. 3, pp. 1-12, 2014.

M. Mühlig, M. Gienger and J. J. Steil, “Interactive imitation learning of object movement skills”, Autonomous Robots, vol. 32, pp. 97-114, 2012.

1995 Diploma: Electrical Engineering, Ruhr-University Bochum, Germany
2001 PhD: Electrical Engineering, Ruhr-University Bochum, Germany

Evolutionary Computation
Artificial Intelligence
Data Mining

R. Cheng, Y. Jin, M. Olhofer and B. Sendhoff, “A Reference Vector Guided Evolutionary Algorithm for Many-objective Optimization” in IEEE Transactions on Evolutionary Computation, 20(5): 773-791, 2016.

M. Bujny, N. Aulig, M. Olhofer, and F. Duddeck, “Hybrid evolutionary approach for level set topology optimization”, in Evolutionary Computation (CEC), 2016 IEEE Congress on, 2016, pp. 5092–5099.

O. Smalikho and M. Olhofer, “Adaptive System Design by a Simultaneous Evolution of Morphology and Information Processing”, in Simulated Evolution and Learning, Springer, 2014, pp. 25–36.

2008 Diploma: Computer Science and Business Administration, University of Mannheim, Germany
2008-2010: Technical Consultant at Hewlett-Packard
2016 PhD: Dr.rer.pol., (Computer Science and Business Administration) University of Mainz, Germany

Unsupervised representation learning
Intelligent, learning agents (reinforcement learning)
Behavior planning for autonomous vehicles

T. Puphal, M. Probst and J. Eggert, “Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving”, IEEE Trans. on Intell. Veh., 2019.

T. Puphal, M. Probst, Y. Li, Y. Sakamoto and J. Eggert, “Optimization of Velocity Ramps with Survival Analysis for Intersection Merge-Ins”, in Proc. IEEE Intell. Veh. Symp., 2018.

M. Probst, F. Rothlauf and J. Grahl, “Scalability of using Restricted Boltzmann Machines for combinatorial optimization”, European Journal of Operational Research vol. 256, no. 2, pp. 368-383, 2017.

2016 MSc: Mechatronics, Technische Universität Darmstadt, Germany
2021 PhD: Electrical Engineering, Technische Universität Darmstadt, Germany (“Driving Risk Models for Predicting, Planning and Warning”)

Situation Prediction
Risk Models
Motion Planning

T. Puphal, M. Probst, Y. Li, Y. Sakamoto and J. Eggert, “Optimization of Velocity Ramps with Survival Analysis for Intersection Merge-Ins”, in Proc. IEEE Intell. Veh. Symp., 2018.

T. Puphal, M. Probst and J. Eggert, “Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving”, IEEE Trans. on Intell. Veh., 2019.

J. Eggert and T. Puphal, “Continuous Risk Measures for Driving Support”, JSAE Int. Journ. of Autom. Eng., 2018.

Patent Officer

IP Management and Strategy

K. Rajan, K. Bejtka, S. Bocchini, D. Perrone, A. Chiappone, I. Roppolo, C. F. Pirri, C. Ricciardi and A. Chiolerio, “Highly performing ionic liquid enriched hybrid RSDs”, J. Mater. Chem. C, 2017, 5, pp 6144-6155.

K. Rajan, S. Bocchini, A. Chiappone, I. Roppolo, D. Perrone, M. Castellino, K. Bejtka, M. Lorusso, C. Ricciardi C. F. Pirri, and A. Chiolerio, “WORM and bipolar inkjet printed resistive switching devices based on silver nanocomposites”, Flex. Print. Electron., 2017, 2, 024002.

K. Rajan, I. Roppolo, K. Bejtka, A. Chiappone, S. Bocchini, D. Perrone, C. F. Pirri, C. Ricciardi and A. Chiolerio, “Performance comparison of hybrid resistive switching devices based on solution-processable nanocomposites”, Appl. Surf. Sci., 2018, Vol. 443, pp 475-483.

Studied Physics and Neuroinformatics at U Bochum and joined Honda in 1998.
Completed PhD in Computational Neuroscience in 2003 at U Bielefeld (Professor Helge Ritter).

Energy Management & Smart Charging
Digital Twins
Human-AI cooperation
Fairness
Many-objective Optimization and Multi-Criteria Decision Making

Christiane Attig, Johannes Varga, Tim Schrills, Tobias Rodemann, Günther Raidl: Annoyance Modeling in Cooperative Personnel Scheduling, 17th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, 2026

Steffen Limmer, Christiane Attig , Tobias Rodemann: A Hierarchical Controller as an Alternative Multi-Objective EV Charging Manager
16th International Conference on Power, Energy, and Electrical Engineering (CPEEE), 2026

Jens Engel et al., A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning,
Scientific Data, 2025

Jose Almeida, Joao Soares, Fernando Lezama, Steffen Limmer, Tobias Rodemann, Zita Vale: A Systematic Review of Explainability in Computational Intelligence for Optimization, Computer Science Review, 2025

2007 Diploma: Computer Science, Technische Universität Darmstadt, Germany
2017 PhD: Computer Science, University of Massachusetts Amherst, USA (“Belief-Space Planning for Resourceful Manipulation and Mobility”)

Robotics
Probabilistic Planning
Manipulation
Teleoperation
Uncertainty Communication

A. Belardinelli, AR. Kondapally, D. Ruiken, D. Tanneberg, T. Watabe, “Intention estimation from gaze and motion features for human-robot shared-control object manipulation”, International Conference on Intelligent Robots and System (IROS), 2022.

M. Gienger, D. Ruiken, T. Bates, M. Regaieg, M. MeiBner, J. Kober, P. Seiwald, A.C. Hildebrandt, “Human-robot cooperative object manipulation with contact changes”, International Conference on Intelligent Robots and System (IROS), 2018.

D. Ruiken, T. Liu, T. Takahashi, and R. Grupen, “Reconfigurable Tasks in Belief-Space Planning”, 16th IEEE-RAS International Conference on Humanoid Robots, Cancun, Mexico, 2016.

D. Ruiken, JM. Wong, T. Liu, M. Hebert, T. Takahashi, M. Lanighan, and R. Grupen, “Affordance-Based Active Belief: Recognition using Visual and Manual Actions”, International Conference on Intelligent Robots and System (IROS), Daejeon, Korea, 2016.

D. Ruiken, M. Lanighan, and R. Grupen, “Postural Modes and Control for Dexterous Mobile Manipulation: the UMass uBot Concept”, 13th IEEE-RAS International Conference on Humanoid Robots, Atlanta, USA, 2013.

Ahmed R. Sadik holds a PhD in Informatics and Computer Science from the University of Rostock and Fraunhofer IGD, an MSc in Machine Automation from Tampere University of Technology, and a BSc in Mechatronics Engineering from Ain Shams University. He has extensive experience in designing and deploying responsible AI frameworks and research-driven solutions across interdisciplinary domains, including e-mobility, Industry 4.0, and the Internet of Things (IoT). His work focuses on leveraging Large Language Models (LLMs) to develop adaptive, context-aware architectures that combine ethical considerations with technological innovation. He also investigates the automated integration of LLM into software and systems engineering to enable intelligent, scalable, and accountable solutions. His multidisciplinary background bridges scientific research and industrial innovation, supporting the development of advanced, human-centered technologies.

Autonomous Systems Architecture
Human-AI Interaction
System Engineering
Software Defined Vehicle
Model-Driven Engineering
System of Systems
Software Engineering
Intelligent System Architectures
Industry 4.0, Internet of Things (IoT)
Adaptive Systems
Federated Learning
Machine Learning
Explainable and Trustworthy AI

For a complete and up-to-date list of publications, please refer to his Google Scholar profile:
https://scholar.google.com/citations?user=p7fiTXkAAAAJ

2002 Diploma: Physics, Technische Universitär Darmstadt, Germany
2008 PhD: Theoretical Condensed Matter Physics, Technical University Darmstadt, Germany
2009-2011: Post-doc, Technical University Dortmund, Germany

Quantum computing
Machine learning, optimization

C. Priester, S. Schmitt, and T.P. Peixoto, “Limits and Trade-Offs of Topological Network Robustness”, PLOS ONE 9, e108215, 2014.

S. Schmitt and F.B. Anders, “Nonequilibrium Zeeman-splitting in quantum transport through nanoscale junctions”, Phys. Rev. Lett. 107, 056801, 2011.

S. Schmitt, “Non-Fermi-liquid signatures in the Hubbard model due to van Hove singularities”, Phys. Rev. B 82, 155126, 2010.

2018 MSc.: Automotive Mechatronics, Technische Universität Darmstadt, Germany

Mechatronics
Additive Manufacturing
Human-Robot Interaction

Martin Weigel, Oliver Schön, Herbert Janßen, “Evaluation of Body-Worn FPCBs with Bluetooth Low Energy, Capacitive Touch, and Resistive Flex Sensing”, ACM UbiComp/ISWC ’20, 2020

2006: Diploma in Computer Science, Guest Scientist HRI
2010: PhD in Cognitive Robotics
2011: Project Leader i-ACC (predictive ADAS)
2015: Assistant Manager
2018: Manager & Chief Scientist & Compliance Officer
2020: Innovation Director
2021: Director Innovation & Director Coordination Global HRI
2024: Vice President HRI-EU

Translating scientific insights into valuable innovative products

S. Bonnin, T. Weisswange, F. Kummert and J. Schmuedderich, “General behavior prediction by a combination of situation specific models”, IEEE Trans. Intell. Transp. Syst., vol. 15, issue 4, pp. 1-11, 2014.

J. Schmuedderich et al., “A novel approach to driver behavior prediction using scene context and physical evidence for intelligent adaptive cruise control (i-ACC) “, Proc. 3rd Int. Symp. FAST-zero, 2015, pp. 85-92.

J. Schmuedderich et al., “Estimating object proper motion using optical flow, kinematics, and depth information.” IEEE Trans. Systems, Man, and Cybernetics, Part B (Cybernetics), vol.  38, issue 4, pp. 1139-1151, 2008.

Intelligent systems, computational and artificial intelligence including evolutionary computation
Industrial engineering optimization and design
Robust heuristic optimization
Cooperative Intelligence

B. Sendhoff and H. Wersing, “Cooperative Intelligence – A Humane Perspective,” 2020 IEEE International Conference on Human-Machine Systems (ICHMS), Rome, Italy, 2020, pp. 1-6, doi: 10.1109/ICHMS49158.2020.9209387.

H. Beyer and B. Sendhoff, “Robust optimization – A comprehensive survey”, Computer Methods in Applied Mechanics and Engineering, vol. 196, issue 33, pp. 3190-3218, 2007.

Y. Jin, M. Olhofer and B. Sendhoff, “A framework for evolutionary optimization with approximate fitness functions”, IEEE Transactions on Evolutionary Computation, vol. 6, issue 5, pp. 481-494, 2002.

2013 B.Sc.: Computer Science, Technische Universität Darmstadt
2015 M.Sc.: Computer Science, Minor: Biological Psychology, Technische Universität Darmstadt
2020 Ph.D.: Computer Science, Technische Universität Darmstadt (“Understand-Compute-Adapt: Neural Networks for Intelligent Agents”)

Machine Learning
Robot Learning
Lifelong and Autonomous Learning
Biological(ly-inspired) Learning

Keller, L.; Tanneberg, D.; & Peters, J. (2025). “Neuro-symbolic imitation learning: Discovering symbolic abstractions for skill learning”, IEEE International Conference on Robotics and Automation (ICRA)

Tanneberg, D.; Rueckert, E.; Peters, J. (2020), “Evolutionary training and abstraction yields algorithmic generalization of neural computers”, Nature Machine Intelligence

Tanneberg, D.; Peters, J.; Rueckert, E. (2019), “Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks”, Neural Networks

2009M.F.A: Interaction Design, Zhejiang University, China
2017 PhD: Eindhoven University of Technology, the Netherlands

Automotive HCI
Human-Robot Interaction
Explainable AI

C. Wang, J. Terken, J. Hu and M. Rauterberg, “Likes and dislikes on the road: a social feedback system for improving driving behavior”, Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2016, pp. 43-50.

C. Wang, J. Terken, J. Hu, “CarNote: reducing misunderstanding between drivers by digital augmentation”, Proceedings of the 22nd International Conference on Intelligent User Interfaces, 2017, pp. 85-94.

2008 Diploma in Bioinformatics, Goethe University Frankfurt
2012 Ph.D. in Computational Neuroscience, Goethe University and the Frankfurt Institute for Advanced Studies
Since 2011 Honda Research Institute Europe GmbH, currently as a Principal Scientist and Group Lead.

Interaction and cooperation between humans and artificial intelligence across fields like robotics, machine learning, cognitive modelling, and intelligent transportation

Weisswange, T. H., Javed, H., Dietrich, M., Jung, M. F., & Jamali, N. (2026). Design Implications for Robots That Facilitate Groups—A Scoping Review on Improving Group Interactions through Directed Robot Action. ACM Transactions on Human-Robot Interaction, 15(2), 1–108. https://doi.org/10.1145/3777455

Rother, D., Herbert, F., Kalter, F., Koert, D., Pajarinen, J., Peters, J., & Weisswange, T. H. (2025). Entropy based blending of policies for multi-agent coexistence. Autonomous Agents and Multi-Agent Systems, 39, 27. https://doi.org/10.1007/s10458-025-09707-7

Weisswange, T. H., Rebhan, S., Bolder, B., Steinhardt, N. A., Joublin, F., Schmuedderich, J., & Goerick, C. (2021). Intelligent Traffic Flow Assist: Optimized Highway Driving Using Conditional Behavior Prediction. IEEE Intelligent Transportation Systems Magazine, 13(2), 20–38. https://doi.org/10.1109/MITS.2019.2898969

2018 MSc.: Automotive Mechatronics, Technische Universität Darmstadt, Germany

Automated Driving and Intelligent Vehicles
Behavior and Trajectory Planning
Control Theory

W. Wachenfeld, P. Junietz, R. Wenzel and H. Winner. “The worst-time-to-collision metric for situation identification.” In 2016 IEEE Intelligent Vehicles Symposium (IV), pp. 729-734. IEEE, 2016.

Heiko received his PhD from the University of Bielefeld in 2000.
He joined Honda R&D in 2000 and moved in 2003 to the newly founded Honda Research Institute Europe.
In 2017 he was awarded an honorary professorship at the University of Bielefeld.

Incremental Learning
Mental Models in Human Robot Interaction
Cooperative Intelligence

Sendhoff, B., & Wersing, H. (2020). Cooperative intelligence-a humane perspective. In 2020 IEEE international conference on human-machine systems (ICHMS) (pp. 1-6). IEEE.

Hindemith, L., Wiebel-Herboth, C. B., Wersing, H., Wrede, B., & Vollmer, A. L. (2025). Improving HRI through robot architecture transparency. International Journal of Social Robotics, 17(12), 2981-3001.

Losing, V., Hammer, B., & Wersing, H. (2018). Incremental on-line learning: A review and comparison of state of the art algorithms. Neurocomputing, 275, 1261-1274.

2010 Diploma: Psychology, Eberhard-Karls University Tuebingen, Germany
2014 PhD: Psychology, Justus-Liebig University, Giessen, Germany (“Visual perception of materials and material properties”)
2014-2016: Post-doc Psychology, Technical University Berlin, Germany

Human-Machine Interaction and Cooperation
Perception & Psychophysics

C. Wiebel*, G. Aguilar* and M. Maertens, „Maximum likelihood difference scales represent perceptual magnitudes and predict appearance matches“, Journal of Vision, 17(4):1, 2017.

R. Fleming, C. Wiebel and K. Gegenfurtner, “Perceptual qualities and material classes”, Journal of Vision, 13(8):9, 2013.

C. Wiebel, M. Valsecchi and K. Gegenfurtner, “The speed and accuracy of material recognition in natural images”, Attention, Perception & Psychophysics, 75(5), pp. 954-966, 2013.

2012: Diploma Psychology, Goethe-University Frankfurt, Germany,
2017: MSc Computer Science, Goethe-University Frankfurt, Germany
2018: PhD Computer Science, Brain Imaging Center, Goethe-University Frankfurt, Germany

Statistics and Machine Learning
Information Theory
Human-Machine Interaction and Cooperation

Christiane Attig, Patricia Wollstadt, Thomas Franke, Tim Schrills, Christiane Wiebel , “More than Task Performance: Developing New Criteria for Successful Human-AI Teaming Using the Cooperative Card Game Hanabi”, CHI EA ’24: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, no. 245, pp. 1-11, 2024.

David Ehrlich, Kyle Poland, Abdullah Makkeh, Felix Lanfermann, Patricia Wollstadt, Michael Wibral , “Partial Information Decomposition for Continuous Variables based on Shared Exclusions: Analytical Formulation and Estimation”, Physical Review E, vol. 110, 2024.

Patricia Wollstadt, Sebastian Schmitt, Michael Wibral , “A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition”, Journal of Machine Learning Research, vol. 24, no. 131, pp. 1-44, 2023.

Christiane Wiebel, Matti Krüger, Patricia Wollstadt , “Measuring cooperation in Hanabi agents using information theory”, International Conference on Hybrid Human-Artificial Intelligence (HHAI 2023) , 2023.

I am a senior scientist at Honda Research Institute EU and a visiting researcher at Imperial College London. I was an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow funded by Schmidt Futures. I received my Ph.D. degree from Imperial College London. My research has been accepted at Science Robotics and T-RO.

I have been awarded The UK Best PhD in Robotics Award 2020 1st place held by Advanced Robotics @ Queen Mary, and Best Research Paper (Early Career Researcher), 2025 AI & Robotics Research Awards held by UKRI. More information can be found on my personal website: https://fan6zh.github.io/

Multi-modal Robot Manipulation
Sim-to-Real
Self-Supervised Learning
Flow Generative Models

“Learning Robot Manipulation from Audio World Models” Fan Zhang, Michael Gienger, arXiv, 2025.

“Affordance-based Manipulation with Flow Matching” Fan Zhang, Michael Gienger, arXiv, 2024.