Marcus Martens
Trailblazer
Division of Research and Innovation
Eligible to supervise Masters and PhD - email supervisor to discuss availability.
I am researcher, innovator and generalist deploying optimization and machine learning techniques for complex challenges. My background is in computer science, firmly grounded in the theory of algorithms and complexity. I like to apply science and during my career I had the pleasure to collaborate and publish together with experts from various domains, including network science, epidemic modelling, neuroscience, cyber-security, gaming and space. I am known for my contributions and participations at the Global Trajectory Optimization Challenge (GTOC) and the organisation of various machine learning challenges on the Kelvins portal of the European Space Agency (ESA). My current focus is on the application of artificial intelligence towards space applications, such as pose estimation, trajectory optimization, spacecraft autonomy and space domain awareness.
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Appointments
Date Position Institution name 2024 - ongoing Senior Research Associate University of Adelaide 2021 - 2022 Scientific Crowdsourcing Engineer European Space Research and Technology Centre 2018 - 2021 Internal Research Fellow European Space Research and Technology Centre 2012 - 2013 Young Graduate Trainee European Space Research and Technology Centre -
Awards and Achievements
Date Type Title Institution Name Country Amount 2024 Award Barry Carlton Award IEEE Aerospace and Electronic Systems Society United States 1000 USD 2013 Award HUMIES Gold Medal John Koza United States 4000 USD -
Language Competencies
Language Competency English Can read, write, speak, understand spoken and peer review German Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title 2013 - 2018 Delft University of Technology Netherlands PhD 2008 - 2011 University of Paderborn Germany Master of Science 2005 - 2008 University of Paderborn Germany Bachelor of Science -
Research Interests
Optimisation Astrodynamics and space situational awareness Earth and space science informatics Satellite, Space Vehicle and Missile Design and Testing Space sciences Computer vision and multimedia computation Neural networks Satellite technologies, networks and services Entertainment and gaming Brain Machine learning Artificial Intelligence Artificial Intelligence & Image Processing Artificial Life Evolutionary computation Neural, Evolutionary and Fuzzy Computation
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Journals
Year Citation 2024 Meoni, G., Märtens, M., Derksen, D., See, K., Lightheart, T., Sécher, A., . . . Izzo, D. (2024). The OPS-SAT case: A data-centric competition for onboard satellite image classification. Astrodynamics, 8(4), 507-528.
Scopus42023 Park, T. H., Märtens, M., Jawaid, M., Wang, Z., Chen, B., Chin, T. -J., . . . D’Amico, S. (2023). Satellite Pose Estimation Competition 2021: Results and Analyses. Acta Astronautica, 204, 604-665.
Scopus35 WoS42023 Märtens, M., Izzo, D., Blazquez, E., von Looz, M., Gómez, P., Mergy, A., . . . Shimane, Y. (2023). The fellowship of the Dyson ring: ACT&Friends’ results and methods for GTOC 11. Acta Astronautica, 202, 807-818.
Scopus3 WoS22020 Kisantal, M., Sharma, S., Park, T. H., Izzo, D., Märtens, M., & D'Amico, S. (2020). Satellite pose estimation challenge: Dataset, competition design, and results. IEEE Transactions on Aerospace and Electronic Systems, 56(5), 4083-4098.
Scopus168 WoS762019 Märtens, M., Izzo, D., Krzic, A., & Cox, D. (2019). Super-resolution of PROBA-V images using convolutional neural networks. Astrodynamics, 3(4), 387-402.
Scopus64 WoS382019 Izzo, D., Märtens, M., & Pan, B. (2019). A survey on artificial intelligence trends in spacecraft guidance dynamics and control. Astrodynamics, 3(4), 287-299.
Scopus198 WoS1282017 Märtens, M., Meier, J., Hillebrand, A., Tewarie, P., & Van Mieghem, P. (2017). Brain network clustering with information flow motifs. Applied Network Science, 2(1), 25.
Scopus19 Europe PMC3 -
Book Chapters
Year Citation 2018 Kuipers, F., Märtens, M., van der Hoeven, E., & Iosup, A. (2018). The Power of Social Features in Online Gaming. In K. Lakkaraju, G. Sukthankar, & R. T. Wigand (Eds.), Social Interactions in Virtual Worlds: An Interdisciplinary Perspective (pp. 313-336). Cambridge University Press.
DOI Scopus22016 Izzo, D., Hennes, D., Simões, L. F., & Märtens, M. (2016). Designing complex interplanetary trajectories for the Global Trajectory Optimization competitions. In Springer Optimization and Its Applications (Vol. 114, pp. 151-176). Springer International Publishing.
DOI Scopus41 -
Conference Papers
Year Citation 2022 Park, T. H., Martens, M., Lecuyer, G., Izzo, D., & D'Amico, S. (2022). SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap. In IEEE Aerospace Conference Proceedings Vol. 2022-March (pp. 1-15). Big Sky, MT, USA: IEEE.
DOI Scopus592021 Chen, B., Liu, D., Chin, T. J., Rutten, M., Derksenv, D., Martens, M., . . . Izzo, D. (2021). Spot the GEO satellites: from dataset to Kelvins SpotGEO Challenge. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2021) (pp. 2086-2094). online: IEEE.
DOI Scopus8 WoS12020 Izzo, D., Märtens, M., Öztürk, E., Kisantal, M., Konstantinidis, K., Simões, L. F., . . . Hernando-Ayuso, J. (2020). Gtoc-x: Our plan to settle the galaxy (esa-act). In Advances in the Astronautical Sciences Vol. 171 (pp. 3355-3370). Portland, Oregon: Univelt Inc. 2019 Märtens, M., & Izzo, D. (2019). Neural network architecture search with differentiable cartesian genetic programming for regression. In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 181-182). CZECH REPUBLIC, Prague: ASSOC COMPUTING MACHINERY.
DOI Scopus7 WoS62019 Izzo, D., Öztürk, E., & Märtens, M. (2019). Interplanetary transfers via deep representations of the optimal policy and/or of the value function. In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 1971-1979). CZECH REPUBLIC, Prague: ASSOC COMPUTING MACHINERY.
DOI Scopus17 WoS102017 Meier, J., M�rtens, M., Hillebrand, A., Tewarie, P., & Van Mieghem, P. (2017). Motif-based analysis of effective connectivity in brain networks. In H. Cherifi, S. Gaito, W. Quattrociocchi, & A. Sala (Eds.), Studies in Computational Intelligence Vol. 693 (pp. 685-696). ITALY, Univ Milan, Milan: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus2 WoS32017 Märtens, M., Kuipers, F., & Van Mieghem, P. (2017). Symbolic regression on network properties. In J. McDermott, M. Castelli, L. Sekanina, E. Haasdijk, & P. GarciaSanchez (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10196 LNCS (pp. 131-146). NETHERLANDS, Amsterdam: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus3 WoS22017 Märtens, M., Asghari, H., Van Eeten, M., & Van Mieghem, P. (2017). A time-dependent SIS-model for long-term computer worm evolution. In 2016 IEEE Conference on Communications and Network Security, CNS 2016 Vol. 1 (pp. 207-215). PA, Philadelphia: IEEE.
DOI Scopus4 WoS22016 Martens, M., Van De Bovenkamp, R., & Van Mieghem, P. (2016). Superinfection in Networks. In K. Yetongnon, & A. Dipanda (Eds.), Proceedings - 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015 Vol. 261 (pp. 413-420). THAILAND, University of Bourgogne, the University of Milan, Bangkok: IEEE.
DOI2016 Martens, M., Shen, S., Iosup, A., & Kuipers, F. (2016). Toxicity detection in multiplayer online games. In Annual Workshop on Network and Systems Support for Games Vol. 2016-January (pp. 6 pages). CROATIA, Zagreb: IEEE.
DOI Scopus20 WoS282016 Izzo, D., Hennes, D., Märtens, M., Getzner, I., Nowak, K., Heffernan, A., . . . Sugimoto, Y. (2016). GTOC8: Results and methods of ESA Advanced Concepts Team and JAXA-ISAS. In R. Zanetti, R. P. Russell, M. T. Ozimek, & A. L. Bowes (Eds.), Advances in the Astronautical Sciences Vol. 158 (pp. 4269-4290). CA, Napa: UNIVELT INC.
Scopus12014 Nowak, K., Märtens, M., & Izzo, D. (2014). Empirical performance of the approximation of the least hypervolume contributor. In T. BartzBeielstein, J. Branke, B. Filipic, & J. Smith (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 8672 (pp. 662-671). SLOVENIA, Ljubljana: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus23 WoS242013 Izzo, D., Simões, L. F., Märtens, M., De Croon, G. C. H. E., Heritier, A., & Yam, C. H. (2013). Search for a grand tour of the Jupiter Galilean moons. In C. Blum (Ed.), GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference (pp. 1301-1308). NETHERLANDS, Amsterdam: ASSOC COMPUTING MACHINERY.
DOI Scopus46 WoS242013 Märtens, M., & Izzo, D. (2013). The asynchronous island model and NSGA-II: Study of a new migration operator and its performance. In C. Blum (Ed.), GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference (pp. 1173-1180). NETHERLANDS, Amsterdam: ASSOC COMPUTING MACHINERY.
DOI Scopus31 WoS222011 Cord-Landwehr, A., Degener, B., Fischer, M., Huellmann, M., Kempkes, B., Klaas, A., . . . Wonisch, D. (2011). Collision less Gathering of Robots with an Extent. In I. Cerna, T. Gyimothy, J. Hromkovic, K. Jeffery, R. Kralovic, M. Vukolic, & S. Wolf (Eds.), SOFSEM 2011: THEORY AND PRACTICE OF COMPUTER SCIENCE Vol. 6543 (pp. 178-189). SLOVAKIA, Novy Smokovec: SPRINGER-VERLAG BERLIN.
WoS222011 Cord-Landwehr, A., Degener, B., Fischer, M., Huellmann, M., Kempkes, B., Klaas, A., . . . Wonisch, D. (2011). A New Approach for Analyzing Convergence Algorithms for Mobile Robots. In L. Aceto, M. Henzinger, & J. Sgall (Eds.), AUTOMATA, LANGUAGES AND PROGRAMMING, ICALP, PT II Vol. 6756 (pp. 650-661). SWITZERLAND, ETH Zurich, Zurich: SPRINGER-VERLAG BERLIN.
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Theses
Year Citation 2018 Martens, M. (2018). Information Propagation in Complex Networks: Structures and Dynamics. (PhD Thesis, Delft University of Technology). -
Preprint
Year Citation 2024 Märtens, M., Farries, K., Culton, J., & Chin, T. -J. (2024). Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and
Evaluating Neuromorphic Vision Algorithms.2022 Märtens, M., & Izzo, D. (2022). Symbolic Regression for Space Applications: Differentiable Cartesian
Genetic Programming Powered by Multi-objective Memetic Algorithms.
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Presentation
Date Topic Presented at Institution Country 2020 - ongoing Applying AI to Advancing Space AMLD 2020 AMLD Switzerland -
Review, Assessment, Editorial and Advice
Date Title Type Institution Country 2022 - ongoing Outstanding Reviewer: NeuRIPS 2022 Dataset and Benchmark Peer Review NeuRIPS - 2019 - ongoing Special Issue on Applications of Artificial Intelligence in Aerospace Engineering Editorial Astrodynamics -
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