Academic Education
- Master of Arts Philosophy, University of Münster
- Master of Arts Philosophy of Science, University of Münster
- PhD Computer Science, University of Münster
- Master of Science Chemistry, University of Münster
- Bachelor of Science Physics, University of Münster
- Bachelor of Science Chemistry, University of Münster
Publications
- Becker, M., Butz, M., Lemli, D., Schuck, C., & Risse, B. (). Learning Proposal Distributions in Simulated Annealing via Template Networks: A Case Study in Nanophotonic Inverse Design. in Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, C.-L., Bhattacharya, S., & Pal, U. (ed.), 27th International Conference ICPR 2024: Vol. 27. Pattern Recognition (pp. 188–202). Springer. doi: 10.1007/978-3-031-78186-5_13.
- Tertilt, H., Mensing, J., Becker, M., van der Wiel, W. G., Bobbert, P. A., & Heuer, A. (). Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks. Physical Review Applied, 22 (2). doi: 10.1103/PhysRevApplied.22.024063.
- Schulte, L., Butz, M., Becker, M., Risse, B., & Schuck, C. (). Accelerating Finite-Difference Frequency-Domain Simulations for Inverse Design Problems in Nanophotonics using Deep Learning. Journal of the Optical Society of America B, 41 (4), 1039–1046. doi: 10.1364/JOSAB.506159.
- Brückerhoff-Plückelmann, F., Borras, H., Klein, B., Varri, A., Becker, M., Dijkstra, J., Brückerhoff, M., Wright, CD., Salinga, M., Bhaskaran, H., Risse, B., Fröning, H., & Pernice, W. (). Probabilistic photonic computing with chaotic light. Nature Communications, 15 (1), 10445–10445. doi: 10.1038/s41467-024-54931-6.
- Becker, M., & Risse, B. (). Learned Random Label Predictions as a Neural Network Complexity Metric.. Workshop on Scientific Methods for Understanding Deep Learning @NeurIPS , Vancouver.
- Wendland, D., Becker, M., Brückerhoff-Plückelmann, F., Bente, I., Busch, K., Risse, B., & Pernice, WH. (). Coherent dimension reduction with integrated photonic circuits exploiting tailored disorder. Journal of the Optical Society of America B, 40 (3), B35–B40.
- Butz, M., Leifhelm, A., Becker, M., Risse, B., & Schuck, C. (). A Universal Approach to Nanophotonic Inverse Design through Reinforcement Learning. in Group, O. P. (ed.), CLEO 2023, paper STh4G.3 (p. STh4G.3–STh4G.3). Optica. doi: 10.1364/CLEO_SI.2023.STh4G.3.
- Butz, M., Leifhelm, A., Becker, M., Risse, B., & Schuck, C. (). A Novel Approach to Nanophotonic Black-Box Optimization Through Reinforcement Learning. in DPG (ed.), Q 30 Nano-optics (p. 1–1). Deutsche Physikalische Gesellschaft.
- Brückerhoff-Plückelmann, F., Bente, I., Becker, M., Vollmar, N., Farmakidis, N., Lomonte, E., Lenzini, F., Wright, C. D., Bhaskaran, H., Salinga, M., Risse, B., & Pernice, W. HP. (). Event-driven adaptive optical neural network. Science advances, 9 (42), eadi9127. doi: 10.1126/sciadv.adi9127.
- Purk, M., Fujarski, M., Becker, M., Warnecke, T., & Varghese, J. (). Utilizing a tablet-based artificial intelligence system to assess movement disorders in a prospective study. Scientific Reports, 13 (1). doi: 10.1038/s41598-023-37388-3.
- Becker, M., Drees, D., Brückerhoff-Plückelmann, F., Schuck, C., Pernice, W., & Risse, B. (). Activation Functions in Non-Negative Neural Networks.. Machine Learning and the Physical Sciences Workshop, NeurIPS, New Orleans.
- Becker, M., Riegelmeyer, J., Seyfried, M. D., Ravoo, B. J., Schuck, C., & Risse, B. (). Adaptive Photochemical Nonlinearities for Optical Neural Networks. Advanced Intelligent Systems, 5 (12). doi: 10.1002/aisy.202300229.
- Becker, M., Butz, M., Lemli, D., Schuck, C., & Risse, B. (). Combinatorial Optimization via Memory Metropolis: Template Networks for Proposal Distributions in Simulated Annealing applied to Nanophotonic Inverse Design.. Neural Information Processing Systems (NeurIPS) Workshop on AI for Accelerated Materials Design (AI4Mat-2023), New Orleans.
- Riegelmeyer, J., Eich, A., Becker, M., Risse, B., & Schuck, C. (). Development of a nanophotonic nonlinear unit for optical artificial neural networks. in DPG (ed.), Q 31 Photonics I (pp. 8–9). Deutsche Physikalische Gesellschaft.
- Butz, M., Leifhelm, A., Becker, M., Risse, B., & Schuck, C. (). Inverse Design of Nanophotonic Devices based on Reinforcement Learning. in DPG (ed.), Q 38 Photonics II (p. 2–2). Deutsche Physikalische Gesellschaft.
- Tertilt, H., Bakker, J., Becker, M., de Wilde, B., Klanberg, I., Geurts, B. J., van der Wiel, W. G., Heuer, A., & Bobbert, P. A. (). Hopping-transport mechanism for reconfigurable logic in disordered dopant networks. Physical Review Applied, 17 (6), 064025. doi: 10.1103/PhysRevApplied.17.064025.
- Friedman, R., Khalid, S., Santamaría, CA., Arutyunova, E., Becker, M., Boyd, KJ., Christensen, M., Coimbra, J. TS., Concilio, S., Daday, C., van Eerden, FJ., Fernandes, PA., Gräter, F., Hakobyan, D., Heuer, A., Karathanou, K., Keller, F., Lemieux, MJ., Marrink, SJ., May, ER., Mazumdar, A., Naftalin, R., Pickholz, M., Piotto, S., Pohl, P., Quinn, P., Ramos, MJ., Schiøtt, B., Sengupta, D., Sessa, L., Vanni, S., Zeppelin, T., Zoni, V., Bondar, A.-N., & Domene, C. (). Understanding Conformational Dynamics of Complex Lipid Mixtures Relevant to Biology. Journal of Membrane Biology, 251 (5), 609–631. doi: 10.1007/s00232-018-0050-y.
Dr. Marlon Marijn Becker
