Marcus Martens

Marcus Martens

Trailblazer

Division of Research and Innovation


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.

  • 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.
    DOI
    2023 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.
    DOI Scopus14 WoS4
    2023 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.
    DOI Scopus2 WoS2
    2020 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.
    DOI Scopus127 WoS76
    2019 Märtens, M., Izzo, D., Krzic, A., & Cox, D. (2019). Super-resolution of PROBA-V images using convolutional neural networks. Astrodynamics, 3(4), 387-402.
    DOI Scopus52 WoS38
    2019 Izzo, D., Märtens, M., & Pan, B. (2019). A survey on artificial intelligence trends in spacecraft guidance dynamics and control. Astrodynamics, 3(4), 287-299.
    DOI Scopus167 WoS128
    2017 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.
    DOI Scopus18 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 Scopus2
    2016 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 Scopus38
  • 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 Scopus28
    2021 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 Scopus4 WoS1
    2020 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).
    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 WoS6
    2019 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 Scopus16 WoS10
    2017 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 Scopus1 WoS3
    2017 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 WoS2
    2017 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 (pp. 207-215). PA, Philadelphia: IEEE.
    DOI Scopus4 WoS2
    2016 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 (pp. 413-420). THAILAND, University of Bourgogne, the University of Milan, Bangkok: IEEE.
    DOI
    2016 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 Scopus15 WoS28
    2016 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.
    Scopus1
    2014 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 WoS24
    2013 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 Scopus45 WoS24
    2013 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 Scopus30 WoS22
    2011 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.
    WoS22
    2011 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.
    WoS31
  • Theses

    Year Citation
    2018 Martens, M. (2018). Information Propagation in Complex Networks: Structures and Dynamics. (PhD Thesis, Delft University of Technology).
  • Preprint

    Year Citation
    2022 Märtens, M., & Izzo, D. (2022). Symbolic Regression for Space Applications: Differentiable Cartesian
    Genetic Programming Powered by Multi-objective Memetic Algorithms.
  • 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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