2011 Diploma: Electrical Engineering and Information Technology, Technical University Darmstadt, Germany
2017 PhD: Dr.-Ing., Electrical Engineering and Information Technology, Technical University Darmstadt, Germany
Machine Learning and Neural Networks
N. Aulig and M. Olhofer, “State-based Representation for Structural Topology Optimization and Application to Crashworthiness”, Evolutionary Computation (CEC), 2016 IEEE Congress on, pp. 1642-1649, 2016.
N. Aulig and M. Olhofer, “Evolutionary Computation for Topology Optimization of Mechanical Structures: An Overview of Representations”, Evolutionary Computation (CEC), 2016 IEEE Congress on, pp. 1948-1955, 2016.
N. Aulig, S. Menzel, E. Nutwell and D. Detwiler, “Preference-based Topology Optimization of Body-in-white Structures for Crash and Static Loads”, 14th LS-DYNA International Conference, 2016.
2005 Diploma: Nachrichtentechnik
2006 MSc: Information Technology
Sensor technology, especially touch sensors for robots
Embedded systems communication
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
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.
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.
Diploma: Rhein-Main University of Applied Science, Germany
PhD: Brunel University London, United Kingdom
J. Deigmoeller, J. Eggert, “Stereo Visual Odometry without Temporal Filtering”, German Conf. on Pattern Recognition, 2016, vol. 9796, pp 166-175.
J. Deigmoeller, H. Janssen, O. Fuchs and J. Eggert, “Monocular rear approach indicator for motorcycles”, Proc. VISAPP Conf., 2014, vol. 3, pp. 474-480.
H. Gandhi, J. Deigmoeller and N. Einecke, “Detection of Camera Artifacts from Camera Images”, IEEE Trans. Intell. Transp. Syst., 2014, vol. 15, issue 4, pp. 603-610.
2010 Diploma: Mechatronics, University of Applied Science, Nuernberg, Germany
2013 M.A. Philosophy of Technology, Technical University Darmstadt, Germany
Ethics of Artificial Intelligence
Philosophy of Technology
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, “An Interdisciplinary Approach on the Mediating Character of Technologies for Recognizing Human Activity”, Philosophies, vol. 1, issue 1, 2015, pp. 55-67.
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.
Diploma: Electrical EngineeringSCIENTIFIC INTEREST:
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. Luecke and J. Eggert, “Expectation truncation and the benefits of preselection in training generative models”, Journal of Machine Learning Research (JMLR) 10, 2010, pp. 2855-2900.
T. Guthier, V. Willert and J. Eggert, “Topological sparse learning of dynamic form patterns”, Neural Comput., vol. 27, no. 1, pp. 42-73, 2015.
J. Eggert, S. Klingelschmitt and F. Damerow, “The Foresighted Driver: Future ADAS based on generalized predictive risk Estimation”, FAST-Zero Symposium, Gothenburg, Sweden, 2015, pp. 93-100.
2006 Diploma: Technical University Ilmenau, Germany
2012 PhD: Technical University Ilmenau, Germany
N. Einecke and J. Eggert, “A multi-block-matching approach for stereo”, Proc. Intelligent Vehicles Symp., Seoul, 2015, pp. 585-592.
N. Einecke and J. Eggert, “Stereo image warping for improved depth estimation of road surfaces”, Proc. Intelligent Vehicles Symp., Gold Coast City, 2013, pp. 189-194.
N. Einecke and J. Eggert, “A two-stage correlation method for stereoscopic depth estimation”, Proc. Int. Conf. Digital Image Computing: Techniques and Applications, Sydney, 2010, pp. 227-234.
L. Fischer, B. Hammer and H. Wersing, “Optimal local rejection for classifiers”, Neurocomputing, vol. 214, pp. 445-457, 2016.
L. Fischer, B. Hammer and H. Wersing, “Efficient rejection strategies for prototype-based classification”, Neurocomputing, vol. 169, pp. 334-342, 2015.
L. Fischer, B. Hammer and H. Wersing, “Online Learning for an Adaptation to Confidence Drift”, In Proceedings of International Joint Conference on Neural Networks, Vancouver, Canada, IJCNN, IEEE, 2016, pp. 748-755.
2015 MSc: Technical University Darmstadt, GermanySCIENTIFIC INTEREST:
Heterogeneous Sensor Fusion
F. Damerow, B. Flade and J. Eggert, “Extensions for the Foresighted Driver Model: Tactical Lane Change, Overtaking and Continuous Lateral Control”, Proc. IEEE Intell. Veh. Symp., 2016, pp. 186-193.
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.
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.
Diploma: Computer Science, Brandenburg Technical University Cottbus, Germany
PhD: Dr.rer.nat. (Theoretical Biology), Humboldt University, Berlin, Germany
2012 MSc: Mechanical and Process Engineering, Technical University Darmstadt, GermanySCIENTIFIC INTEREST:
Deep Neural Networks
Many Objective Optimization
T. Friedrich and S. Menzel, “A cascaded evolutionary multi-objective optimization for solving the unbiased universal electric motor family problem”, 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3184-3191, 2014.
M. Heiderich, T. Friedrich and M. Nguyen, “VEHICLE HANDLING OPTIMIZATION AND CONTROL – New approach for improvement of the vehicle performance by using a simulation-based optimization and evaluation method (Neuer Ansatz zur Verbesserung des Fahrverhaltens durch Verwendung einer simulationsbasierten Optimierungs- und Beurteilungsmethode)”, 7th International Munich Chassis Symposium 2017, pp. 279-293, 2017.
2006 Diploma: Computer Engineering, Technical University Berlin, Germany
2012 PhD: German Aerospace Center, Oberpfaffenhofen, Germany
2013-2015: Continental Automotive GmbH
Human Pose Recognition and Head-Eye-Tracking
S. Fuchs, M. Suppa, and O. Hellwich, “Compensation for multipath in ToF camera measurements supported by photometric calibration and environment integration”, Proc. International Conference on Computer Vision 2013, pp. 31-41.
S. Fuchs, S. Haddadin, M. Keller, S. Parusel, A. Kolb, and M. Suppa, “Cooperative bin-picking with ToF-camera and impedance controlled DLR lightweight robot III”, Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems 2010, pp. 4862-4867.
S. Fuchs and G. Hirzinger, “Intrinsic and depth calibration of ToF cameras”, Proc. IEEE Conference on Computer Vision and Pattern Recognition 2008.
1998 Diploma: Mechanical Engineering, Technical University Munich, Germany
2004 PhD: Dr.-Ing.. Technical University Munich, Germany
(“Design and Realization of a Biped Walking Robot”)
S. Manschitz, J. Kober, M. Gienger and J. Peters, “Learning movement primitive attractor goals and sequential skills from kinesthetic demonstrations”, Rob. Auton. Syst., vol. 74, pp. 97-107, 2015.
S. Dragiev, M. Toussaint and M. Gienger, “Gaussian process implicit surfaces for shape estimation and grasping”, Proc. IEEE Int. Conf. Robotics and Automation. (ICRA), 2011, pp. 2845-2850.
M. Gienger, H. Janßen and C. Goerick, “Task-oriented whole body motion for humanoid robots”, Proc. 5th IEEE-RAS Int. Conf. Humanoids. (Humanoids), 2005, pp. 238-244.
Planning under uncertainty
M. Mühlig, A. Hayashi, M. Gienger, S. Iba and T. Yoshiike, “Receding Horizon Optimization of Robot Motions generated by Hierarchical Movement Primitives”, Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2014.
1995: INSA de Lyon, Lyon, France
1997 Diploma: University of Karlsruhe, Germany
1998: Zeiss Medicare, Dublin, CA, USA
2003 PhD: University of Karlsruhe, Germany (“Adaptive audio-visual speech recognition”)
2000/2001: gipsa Lab Grenoble, France
Intelligent user interfaces
M. Heckmann, ” Audio-visual word prominence detection from clean and noisy speech”, Computer, Speech and Language, to be published.
A. Schnall, M. Heckmann, “Comparing speaker independent and speaker adapted classification for word prominence detection”, Proc. IEEE Spoken Language Technology Workshop (SLT), 2016 IEEE.
N. Schömig, M. Heckmann, H. Wersing, C. Maag, A. Neukum, “Assistance-on-demand: A speech-based assistance system for urban intersections”, Proc. 8th Int. Conf. Automotive User Interfaces and Interactive Vehicular Applications Adjunct., ACM, 2016.
Diploma: PhysicsSCIENTIFIC INTEREST:
Motorcycle assistance systems
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
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.
2008 Diploma: Computer Science, Friedrich-Schiller-University Jena, Germany
2015 PhD: Dr.-Ing., Friedrich-Alexander-University Erlangen, Germany
S. Limmer and D. Fey, “Comparison of Common Parallel Architectures for the Execution of the Island Model and the Global Parallelization of Evolutionary Algorithms”, Concurrency Computat.: Pract. Exper., vol. 29, issue 9, 2016.
S. Limmer and D. Fey, “Investigation of Strategies for an Increasing Population Size in Multi-objective CMA-ES”, IEEE CEC 2016, 476-483, 2016.
S. Limmer, M. Srba and D. Fey, “Performance Investigation and Tuning in the Interoperable Cloud4E Platform”, Euro-Par 2014 FedICI Workshop, LNCS, vol. 8806, pp. 86-97, 2014.
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.
1995 Diploma: Electrical Engineering, Ruhr-University Bochum, Germany
2001 PhD: Electrical Engineering, Ruhr-University Bochum, Germany
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)
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.
M. Probst, “Denoising Autoencoders for Fast Combinatorial Black Box Optimization”, Companion Proc. Genetic and Evolutionary Computation Conference (GECCO), Madrid, Spain, 2015, pp. 1459-1460.
M. Probst, F. Rothlauf and J. Grahl, “An implicitly parallel EDA based on Restricted Boltzmann Machines”, Proc. Genetic and Evolutionary Computation Conference (GECCO), Vancouver, Canada, 2014, pp. 1055-1062.
2016 MSc: Automotive Mechatronics, Technical University Darmstadt, GermanySCIENTIFIC INTEREST:
J. Eggert and T. Puphal, “Continuous Risk Measures for Driving Assistance”, Proc. 4th Int. Symp. FAST-zero, 2017.
J. Eggert, D. Aguirre Salazar, T. Puphal and B. Flade, “Relational Local Dynamic Maps for Driving Situation Analysis”, Proc. 4th Int. Symp. FAST-zero, 2017.
F. Damerow, T. Puphal and J. Eggert, “Risk-based Driver Assistance for Approaching Intersections of Limited Visibility”, Proc. 1st Int. Conf. Veh. Electron. Safety (ICVES), 2017.
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
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.
C. Karaoguz, T. Rodemann, B. Wrede and C. Goerick, “Learning Information Acquisition for Multitasking Scenarios in Dynamic Environments,” in IEEE Transactions on Autonomous Mental Development, vol. 5, no. 1, pp. 46-61, 2013.
T. Rodemann, M. Heckmann, F. Joublin, C. Goerick and B. Scholling, “Real-time Sound Localization With a Binaural Head-system Using a Biologically-inspired Cue-triple Mapping,” 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, 2006, pp. 860-865.
2002 Diploma: Physics, Technical University Darmstadt, Germany
2008 PhD: Theoretical Condensed Matter Physics, Technical University Darmstadt, Germany
2009-2011: Post-doc, Technical University Dortmund, Germany
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.
2006 Diploma: Computer Science, Bielefeld University, Germany
2009 PhD: Dr.-Ing. (Cognitive Robotics), Bielefeld University, Germany
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, Technical University Darmstadt, Germany
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.
MSc: EngineeringSCIENTIFIC INTEREST:
Traffic big data analysis
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
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.
T.H. Weisswange, B. Bolder, J. Fritsch, S. Hasler, C. Goerick, “An Integrated ADAS for Assessing Risky Situations in Urban Driving”, in Proc. IEEE Intell. Veh. Symp. (IV 2013), pp. 292-297.
T.H. Weisswange, C. A. Rothkopf, T. Rodemann, J. Triesch, “Bayesian cue integration as a developmental outcome of reward mediated learning”, PLoS ONE, vol. 6, issue 7, e21575, 2011.
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
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.