2022 MSc.: Electrical Engineering, Technische Universität Darmstadt

Cooperative Driving
Behavior Planning and Prediction
Human-Machine Interfaces for Autonomous Vehicles

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

Human-AI Teaming
User Diversity
Psychological Basic Needs

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.

C. Attig and T. Franke, “I track, therefore I walk – Exploring the motivational costs of wearing activity trackers in actual users,” Int. J. Hum.-Comput. Stud., vol. 127, pp. 211–224, 2019.

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

Diploma: Physics, Bergische University Wuppertal, Germany
PhD: Dr.rer.nat., Ruhr-University Bochum, Germany
(“Coordination of an Artificial Visual System with Biological Models”)

System design and integration for real-time artifacts
Tracking
Low level system infrastructure (timestamping, vehicle actuation, sensory processing)

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: Physics, Universiteit Leiden.
2022-2025 Postdoc: NWO grant, Quantum Software Consortium, Universiteit Leiden.

Quantum computation and algorithms
Quantum advantage in near-term hardware for real-world applications
(Quantum) Machine learning from quantum data

Nearly-optimal measurement scheduling for partial tomography of quantum states, X. Bonet-Monroig, R. Babbush, T.E. O’Brien, Physical Review X 10, 031064 (2020)

Analyzing variational quantum algorithms with information content, A. Perez-Salinas, H. Wang, and X. Bonet-Monroig, npj Quantum Information 10, 27 (2024)

Performance comparison of optimization methods on variational quantum algorithms, X. Bonet-Monroig, H. Wang, D. Vermetten, B Senjean, C. Moussa,T. Back, V. Dunjko, and T. E. O’Brien, Physical Review A 107, 032407 (2023)

Low-cost error mitigation by symmetry verification, X. Bonet-Monroig, R. Sagastizabal, M. Singh, and T.E. O’Brien, Physical Review A 98, 062339 (2018)

2016 M.Sc.: Mechanical and Process Engineering, Technische Universität Darmstadt

Transport Systems
Agent-based Simulations

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

System Design and Architectures
Software Infrastructures and Integration Middlewares
Intelligent and Modular Systems

A. Ceravola, F. Joublin, M. Dunn, J. Eggert, M. Stein and C. Goerick, “Integrated research and development environment for real-time distributed embodied intelligent systems”, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2006, pp. 1631-1637.

A. Ceravola, M. Stein and C. Goerick, “Researching and developing a real-time infrastructure for intelligent systems – Evolution of an integrated approach”, Robotics and Autonomous Systems 56.1, 2008, pp. 14-28.

B. Dittes, A. Gepperth, A. Ceravola, J. Fritsch and C. Goerick, “Self-management for neural dynamics in brain-like information processing”, Proceedings of the 6th International Conference on Autonomic Computing and Communications, 2009, pp. 57-58.

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.

1994 Diploma: Physics, Technical University Munich, Germany
1999 PhD: Dr.rer.nat., (Cognitive Neuroscience, Theoretical Biophysics), Technical University Munich, Germany (“Theory of Activity-Gated Attention in the Visual Cortex”)

Scene representation and interpretation
Risk prediction, intention estimation, decision making under uncertainty
Learning of common-sense knowledge

J. Eggert, J. Deigmöller, L. Fischer and A. Richter, “Memory Nets: Knowledge Representation for Intelligent Agent Operations in Real World”, International Conference on Knowledge Engineering and Ontology Development, 2019.

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

B. Flade, A. Koppert, G. Ve’lez Isasmendi, M. Nieto, A. Das, D. Be’taille, O. Otaegui, J. Eggert, ” Low-Cost Lane-level self-localization using filtered GNSS and Camera to Map Alignment”, IEEE Intelligent Transportation Systems Magazine, 2019.

2006 Diploma: Technical University Ilmenau, Germany
2012 PhD: Technical University Ilmenau, Germany

Computer Vision
Outdoor Robotics
Efficient Algorithms

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.

N. Einecke, J. Deigmöller, K. Muro, and M. Franzius, “Boundary wire mapping on autonomous lawn mowers”, Field and Service Robotics, Zurich, 2017, pp. 351-365.

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

2018 MSc.: Mechatronics, Technische Universität Darmstadt, Germany

Technology Modernization
Agile
Software

2022 MSc: Quantum Simulation on Quantum Computers

Quantum Optimization
Quantum Simulation
Multiple-Objective Optimization

2020 MSc: Electrical Engineering and Information Technology, Technische Universität Darmstadt, Germany

Energy Management Systems
Model Predictive Control
Energy system optimization

J. Engel, T. Schmitt, T. Rodemann, and J. Adamy, “Hierarchical scenario-based economic model predictive control approach for a building energy management system with EV charging”, IEEE Transactions on Smart Grid, 2022.

T. Schmitt, J. Engel, T. Rodemann, “Regression-Based Model Error Compensation for Hierarchical MPC Building Energy Management System”, Proceedings of the 7th IEEE Conference on Control Technology and Applications (CCTA), 2023.

2015 MSc: Technische Universität Darmstadt, Germany

Digital Cartography
Computer Vision
Global Navigation Satellite System

B. Flade, M. Nieto, G. Velez and J. Eggert, “Lane Detection Based Camera to Map Alignment Using Open-Source Map Data”, Proc. IEEE 21st Int. Conf. Intell. Transp. Syst. (ITSC), 2018, pp. 890-897.

J. Eggert, D. Aguirre Salazar, T. Puphal and B. Flade, “Driving Situation Analysis with Relational Local Dynamic Maps”, Proc. 4th Int. Symp. FAST-zero, 2017.

G. Cao, F. Damerow, B. Flade, M. Helmling and J. Eggert, “Camera to Map Alignment for Accurate Low-Cost Lane-Level Scene Interpretation”, Proc. IEEE 19th Int. Conf. Intell. Transp. Syst. (ITSC), 2016, pp. 498-504.

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.

2016 MSc.: Mechanical Engineering, Technische Universität Darmstadt, Germany
2016-2017: Lhoist Recherche et Developpement, Nivelles, Belgium

Project Coordination
Technology Transfer
Technology Management
Communications

Diploma: Physics

Driver modelling
Motorcycle assistance systems
Learning

J. Deigmoeller, H. Janssen, O. Fuchs and J. Eggert, “Monocular rear approach indicator for motorcycles”, Proc. VISAPP Conf., 2014, vol. 3, pp. 474-480.

N. Magiera, H. Janssen, M. E. Heckmann, H. Winner, “Rider Skill Identification by Probabilistic Segmentation into Motorcycle Maneuver Primitives “, Proc. ITSC 2016.

N. Magiera, H. Janssen, H. Winner,”An Approach for Automatic Riding Skill Identification – Methodology and first Results”, Proc. ifz 2016

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

C. Gläser and F. Joublin, “A Computational Model for Grounding Words in the Perception of Agents”, Proceedings of the 9th IEEE Int. Conf. on Development and Learning (ICDL), Ann Arbor, Mich., 2010, pp. 26-32.

C. Gläser and F. Joublin, “Firing Rate Homeostasis for Dynamic Neural Field Formation”, Journal of Transactions on Autonomous Mental Development archive, IEEE, 2011, Volume 3, Issue 4, pp. 285-299 .

N. Hemion et al., “Integration of Sensorimotor Mappings by Making Use of Redundancies”, Proceedings of the Int. Joint Conf. on Neural Networks (IJCNN), Brisbane, Australia, 2012, pp. 1-8.

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

Artificial Intelligence for Engineering
Computational Creativity
Machine Learning

S. Khodaverdian, F. Lanfermann and J. Adamy, “Root Locus Design for the Synchronization of Multi-Agent Systems in General Directed Networks”, 5th IFAC Workshop on Estimation and Control of Networked Systems (NecSys), Pennsylvania, Philadelphia, USA, 2015.
F. Lanfermann, S. Schmitt, and S. Menzel, “An Effective Measure to Identify Meaningful Concepts in Engineering Design optimization,” 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020.

2008 Diploma: Computer Science, Friedrich-Schiller-University Jena, Germany
2015 PhD: Dr.-Ing., Friedrich-Alexander-University Erlangen, Germany

Evolutionary Optimization
Prediction
Energy Management

S. Limmer and T. Rodemann, “Peak Load Reduction through Dynamic Pricing for Electric Vehicle Charging”, International Journal of Electrical Power & Energy Systems, vol. 113, pp. 117-128, 2019.

S. Limmer and M. Dietrich, “Optimization of Dynamic Prices for Electric Vehicle Charging Considering Fairness,” IEEE SSCI, pp. 2304-2311, 2018.

S. Limmer and T. Rodemann, “Multi-objective optimization of plug-in electric vehicle charging prices,” IEEE SSCI, pp. 2853-2860, 2017.

2020 MSc.: Mechanical Engineering, Karlsruhe Institute of Technology, Germany

Machine Learning, Robotics, 3D Reconstruction.

2014: MSc. Information System Technology, Technische Universität Darmstadt, Germany
2017 PhD: Dr.-Ing., Technische Universität Darmstadt, Germany

Machine Learning
Robotics
Behavior Generation

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.

S. Manschitz, M. Gienger, J. Kober, J. Peters, “Probabilistic Decomposition of Sequential Force Interaction Tasks into Movement Primitives”, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pp. 3920-3927, 2016.

2014 Diploma: Computer Science, Lomonosov Moscow State University, Russia

Information security and data privacy
Privacy-enhancing technologies

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

Evolutionary Optimization
Geometry Processing
Machine Learning

X. Lu, S. Menzel, K. Tang, X. Yao, “Cooperative co-evolution-based design optimization: a concurrent engineering perspective”, in IEEE Transactions on Evolutionary Computation, vol. 22, IEEE, 2017, pp. 173-188.

N. Aulig, E. Nutwell, S. Menzel, D. Detwiler, “Preference-based topology optimization for vehicle concept design with concurrent static and crash load cases”, in Structural and Multidisciplinary Optimization, vol. 57, Springer Berlin Heidelberg, 2018, pp. 251-266.

D. Sieger, S. Gaulik, J. Achenbach, S. Menzel, M. Botsch, “Constrained space deformation techniques for design optimization”, in Computer-Aided Design, vol. 72, Elsevier, 2016, pp. 40-51.

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

Robot imitation learning and movement generation
Robotic systems and integration
Long-term autonomous systems

M. 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”, Auton. Robots, vol. 32, pp. 97-114, 2012.

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

Software Infrastructure and Integration
System Design and Architecture

2018 MSc: Mechanical Engineering, Technical University of Munich, Germany
2022 PhD: Technical University of Munich, Germany (“Knowledge Representation and Ontology Merging in the Context of Automated Production Systems”

Knowledge Representation
Intelligent Systems

Ocker, F., Vogel-Heuser, B., & Paredis, C. J. J. (2022). A framework for merging ontologies in the context of smart factories. Computers in Industry, 135, 103571.

Ocker, F., Vogel-Heuser, B., & Paredis, C. J. J. (2019). Applying semantic web technologies to provide feasibility feedback in early design phases. Journal of Computing and Information Science in Engineering, 19(4).

Ocker, F., Kovalenko, I., Barton, K., Tilbury, D., & Vogel-Heuser, B. (2019). A framework for automatic initialization of multi-agent production systems using semantic web technologies. IEEE Robotics and Automation Letters, 4(4), 4330-4337.

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.

2014 MSc: Nanotechnology, Politecnico Di Torino, Italy
2018 PhD: Italian Institute of Technology, Italy

Intellectual Property Protection
Hybrid Memristors
Nanomaterial Synthesis and Thin Film Deposition

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.

1998 Diploma: Physics (Neuroinformatics), Ruhr-University Bochum, Germany
2003 PhD. Dr.rer.nat.: Technical Sciences, Bielefeld University, Germany

Smart Energy Management
Many-objective Optimization and Multi-Criteria Decision Making
System simulation

R. Cheng, T. Rodemann, M. Fischer, M. Olhofer and Y. Jin, “Evolutionary Many-Objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation”, in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 1, no. 2, pp. 97-111, 2017.

T. Rodemann, “Industrial Portfolio Management for Many-Objective Optimization Algorithms”, IEEE WCCI, 2018, Rio de Janeiro, Brazil.

T. Jatschka, T. Rodemann, G. Raidl, “A Cooperative Optimization Approach for Distributing Service Points in Mobility Applications”, EvoCOP, 2019, LNCS 11452, Springer.

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

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 Robotics and Systems (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.

2013 MSc.: Automation Engineering, Tampere University of Technology, Finland
2019 PhD: University of Rostock, Germany

Information System Architecture
Human-Robot Cooperation
Artificial Software Agents

A. R. Sadik, B. Urban, A Holonic “Control System Design for a Human & Industrial Robot Cooperative Workcell”, In 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC). IEEE, (2016).

A. R. Sadik, B. Urban, Ontology in “Holonic Cooperative Manufacturing: A Solution to Share and Exchange the Knowledge”, In International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management. Springer, (2017).

A. R. Sadik, B.  Urban, “Flow Shop Scheduling Problem and Solution in Cooperative Robotics — Case-Study: One Cobot in Cooperation with One Worker”, Future Internet, (2017).

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

Optimization
Data mining
Machine learning

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.

2017 MSc: Mechatronics, Technische Universität Darmstadt, Germany

Energy Management Systems
Model Predictive Control
Decision Making

T. Schmitt, T. Rodemann, and J. Adamy. Multi-objective model predictive control for microgrids. at – Automatisierungstechnik, 68(8):687 – 702, 2020.

T. Schmitt, T. Rodemann, and J. Adamy. The cost of photovoltaic forecasting errors in microgrid control with peak pricing. Energies, 14(9), 2021.

J. Engel, T. Schmitt, T. Rodemann, and J. Adamy. Hierarchical scenario-based economic model predictive control approach for a building energy management system with EV charging. IEEE Transactions on Smart Grid, 2022.

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

Mechatronics
3D Modeling
Electronics development

2006 Diploma: Computer Science, Bielefeld University, Germany
2009 PhD: Dr.-Ing., (Cognitive Robotics), Bielefeld University, Germany

Behavior Prediction
Situation Understanding
Data Privacy & Secure Computing

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.

1993 Diploma: Physics, Ruhr-University Bochum, Germany
1990-1991: University of Sussex, United-Kingdom
1992-1993: University of Sussex, United-Kingdom
1998 PhD: Dr.rer.nat., Physics, Ruhr-University Bochum, Germany
2007: Honorary Professor, School of Computer Science,
University of Birmingham, United Kingdom
2008: Honorary Professor, Technische Universität Darmstadt, Germany

Computational Intelligence
System Optimization
Cooperative Intelligence

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.

H. Beyer and B. Sendhoff, “Simplify Your Covariance Matrix Adaptation Evolution Strategy”, IEEE Transactions on Evolutionary Computation, 2017.

2005: Computer Science, University of Applied Sciences, Merseburg, Germany

Linux toolchains
build automation
DevOps
IT law & IP compliance management
Video editing

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

Tanneberg, D.; Ploeger, K.; Rueckert, E.; Peters, J. (2021), “SKID RAW: Skill Discovery from Raw Trajectories”, IEEE Robotics and Automation Letters (RA-L)

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: Bioinformatics, Goethe University Frankfurt, Germany
2012 PhD: Frankfurt Institute for Advanced Studies (FIAS) and
Goethe University Frankfurt, Germany (“Development of cue integration with reward-mediated learning”)

Intelligent Transportation Systems
Decision Making & Reinforcement Learning
Cognitive Systems Architecture

T.H. Weisswange, S. Rebhan, B. Bolder, N.A. Steinhardt, F. Joublin, J. Schmuedderich, C.Goerick, “intelligent Traffic Flow Assist: Optimized Highway Driving Using Conditional Behavior Prediction”, IEEE Intell. Transp. Syst. Mag., in press, 2019.

M.C. Buehler, T.H. Weisswange, “Online inference of human belief for cooperative robots”, In Proc. IROS 2018, Madrid, Spain.

S. Bonnin, T.H. 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.

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.

1998 Diploma: Physics, Bielefeld University, Germany
2000 PhD: Dr.rer.nat. Computer Science, Bielefeld University, Germany
2017: Honorary Professor, Bielefeld University, Germany

Incremental and online learning
Human machine interaction
Personalization

V. Losing, B. Hammer and H. Wersing, “KNN Classifier with Self-Adjusting Memory for Heterogeneous Concept Drift”, Proc. IEEE Int. Conf. on Data Mining (ICDM). Best paper award, 2016, pp. 291-300.

S. Kirstein, H. Wersing, H.-M. Gross, and E. Körner, “A Life-Long Learning Vector Quantization Approach for Interactive Learning of Multiple Categories”, Neural Networks vol. 28, pp.90-105, 2012.

H. Wersing and E. Körner, “Learning optimized features for hierarchical models of invariant recognition”, Neural Computation vol. 15(7), pp.1559-1588, 2003.

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.

Diploma: Diploma Psychology, Goethe-University Frankfurt, Germany, MSc Computer Science, Goethe-University Frankfurt, Germany
PhD: Dr. rer. nat., Computer Science, Brain Imaging Center, Goethe-University Frankfurt, Germany

P. Wollstadt, J.T. Lizier, R. Vicente, C. Finn, M. Martínez-Zarzuela, P. Mediano, L. Novelli, M. Wibral, “IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks”, Journal of Open Source Software 4(34), 1081, 2019.

P. Wollstadt, K.K. Sellers, L. Rudelt, V. Priesemann , A. Hutt, F. Fröhlich, M. Wibral, “Breakdown of local information processing may underlie isoflurane anesthesia effects”, PLoS Computational Biology 13 (6), e1005511, 2017.

P. Wollstadt, U. Meyer, M. Wibral, “A Graph Algorithmic Approach to Separate Direct from Indirect Neural Interactions”, PLoS ONE 10(10), e0140530, 2015.