Dr. Charlotte Debus
- Junior Research Group Leader
-
charlotte debus ∂does-not-exist.kit edu
| since 2022 | Junior Research Group Leader | Scientific Computing Center (SCC), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany |
| 2020 - 2022 | Research Associate | Scientific Computing Center (SCC), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany |
| 2019 - 2020 | Research Associate | Institute of Software Technology, German Aerospace Center (DLR), Cologne, Germany |
| 2016 - 2019 | Research associate, project group leader | Department of Translational Radiooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany |
| 2013 - 2016 | PhD Physics | University of Heidelberg |
| 2013 | Trainee | European Synchrotron Radiation Facility (ESRF), Grenoble, France |
| 2010 - 2012 | Master of Science Physics | University of Heidelberg |
| 2011 | Semester abroad | Universidade de Lisboa, Portugal |
| 2007 - 2010 | Bachelor of Science Physics | University of Heidelberg |
List of publications KITopen
Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite Data
Götz, M.; Wessel, E.; Çayoğlu, U.; Jahani, B.; Debus, C.; Cermak, J.; Streit, A.
2025. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–12. doi:10.1109/JSTARS.2025.3557931
Götz, M.; Wessel, E.; Çayoğlu, U.; Jahani, B.; Debus, C.; Cermak, J.; Streit, A.
2025. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–12. doi:10.1109/JSTARS.2025.3557931
Accuracy of an articulated head-and-neck motion model using deep learning-based instance segmentation of skeletal bones in CT scans for image registration in radiotherapy
Walter, A.; Bauer, C. J.; Yawson, A. K.; Hoegen-Saßmannshausen, P.; Adeberg, S.; Debus, J.; Jäkel, O.; Frank, M.; Giske, K.
2025. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 13 (1), 2455752. doi:10.1080/21681163.2025.2455752
Walter, A.; Bauer, C. J.; Yawson, A. K.; Hoegen-Saßmannshausen, P.; Adeberg, S.; Debus, J.; Jäkel, O.; Frank, M.; Giske, K.
2025. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 13 (1), 2455752. doi:10.1080/21681163.2025.2455752
Architectural insights into and training methodology optimization of Pangu-Weather
To, D.; Quinting, J.; Hoshyaripour, G. A.; Götz, M.; Streit, A.; Debus, C.
2024. Geoscientific Model Development, 17 (23), 8873–8884. doi:10.5194/gmd-17-8873-2024
To, D.; Quinting, J.; Hoshyaripour, G. A.; Götz, M.; Streit, A.; Debus, C.
2024. Geoscientific Model Development, 17 (23), 8873–8884. doi:10.5194/gmd-17-8873-2024
Harnessing Orthogonality to Train Low-Rank Neural Networks
Coquelin, D.; Flügel, K.; Weiel, M.; Kiefer, N.; Debus, C.; Streit, A.; Götz, M.
2024. ECAI 2024 : 27th European Conference on Artificial Intelligence 19–24 October 2024, Santiago de Compostela, Spain Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024 ; Proceedings. Ed.: U. Endriss, 2106–2113, IOS Press. doi:10.3233/FAIA240729
Coquelin, D.; Flügel, K.; Weiel, M.; Kiefer, N.; Debus, C.; Streit, A.; Götz, M.
2024. ECAI 2024 : 27th European Conference on Artificial Intelligence 19–24 October 2024, Santiago de Compostela, Spain Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024 ; Proceedings. Ed.: U. Endriss, 2106–2113, IOS Press. doi:10.3233/FAIA240729
Taylor Expansion in Neural Networks: How Higher Orders Yield Better Predictions
Zwerschke, P.; Weyrauch, A.; Götz, M.; Debus, C.
2024. ECAI 2024 – 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024). Ed.: U. Endriss, 2983–2989, IOS Press. doi:10.3233/FAIA240838
Zwerschke, P.; Weyrauch, A.; Götz, M.; Debus, C.
2024. ECAI 2024 – 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024). Ed.: U. Endriss, 2983–2989, IOS Press. doi:10.3233/FAIA240838
Model Fusion via Neuron Transplantation
Öz, M.; Kiefer, N.; Debus, C.; Hörter, J.; Streit, A.; Götz, M.
2024. Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part IV. Ed. by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė, 3–19, Springer Nature Switzerland. doi:10.1007/978-3-031-70359-1_1
Öz, M.; Kiefer, N.; Debus, C.; Hörter, J.; Streit, A.; Götz, M.
2024. Machine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part IV. Ed. by Albert Bifet, Jesse Davis, Tomas Krilavičius, Meelis Kull, Eirini Ntoutsi, Indrė Žliobaitė, 3–19, Springer Nature Switzerland. doi:10.1007/978-3-031-70359-1_1
ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers
Weyrauch, A.; Steens, T.; Taubert, O.; Hanke, B.; Eqbal, A.; Götz, E.; Streit, A.; Götz, M.; Debus, C.
2024. 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore, Singapore, 25-27 June 2024, 1187–1194, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CAI59869.2024.00212
Weyrauch, A.; Steens, T.; Taubert, O.; Hanke, B.; Eqbal, A.; Götz, E.; Streit, A.; Götz, M.; Debus, C.
2024. 2024 IEEE Conference on Artificial Intelligence (CAI), Singapore, Singapore, 25-27 June 2024, 1187–1194, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/CAI59869.2024.00212
perun: Benchmarking Energy Consumption of High-Performance Computing Applications
Gutiérrez Hermosillo Muriedas, J. P.; Flügel, K.; Debus, C.; Obermaier, H.; Streit, A.; Götz, M.
2023. Euro-Par 2023: Parallel Processing. Ed.: J. Cano, 17–31, Springer Nature Switzerland. doi:10.1007/978-3-031-39698-4_2
Gutiérrez Hermosillo Muriedas, J. P.; Flügel, K.; Debus, C.; Obermaier, H.; Streit, A.; Götz, M.
2023. Euro-Par 2023: Parallel Processing. Ed.: J. Cano, 17–31, Springer Nature Switzerland. doi:10.1007/978-3-031-39698-4_2
Surrogate Modelling for Core Degradation in pressurized Water Reactors
Dressner, J.; Götz, M.; Stakhanova, A.; Gabrielli, F.; Debus, C.
2024. Helmholtz Artificial Intelligence Conference (Helmholtz AI 2024), Düsseldorf, Germany, June 12–14, 2024
Dressner, J.; Götz, M.; Stakhanova, A.; Gabrielli, F.; Debus, C.
2024. Helmholtz Artificial Intelligence Conference (Helmholtz AI 2024), Düsseldorf, Germany, June 12–14, 2024
PETNet–Coincident Particle Event Detection using Spiking Neural Networks
Debus, J.; Debus, C.; Dissertori, G.; Götz, M.
2024. 2024 Neuro Inspired Computational Elements Conference (NICE), 9 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/NICE61972.2024.10549584
Debus, J.; Debus, C.; Dissertori, G.; Götz, M.
2024. 2024 Neuro Inspired Computational Elements Conference (NICE), 9 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/NICE61972.2024.10549584
Reporting electricity consumption is essential for sustainable AI
Debus, C.; Piraud, M.; Streit, A.; Theis, F.; Götz, M.
2023. Nature Machine Intelligence, 5 (11), 1176–1178. doi:10.1038/s42256-023-00750-1
Debus, C.; Piraud, M.; Streit, A.; Theis, F.; Götz, M.
2023. Nature Machine Intelligence, 5 (11), 1176–1178. doi:10.1038/s42256-023-00750-1
Essential parameters needed for a U-Net-based segmentation of individual bones on planning CT images in the head and neck region using limited datasets for radiotherapy application
Yawson, A. K.; Walter, A.; Wolf, N.; Klüter, S.; Hoegen, P.; Adeberg, S.; Debus, J.; Frank, M.; Jäkel, O.; Giske, K.
2024. Physics in Medicine & Biology, 69 (3), Art.-Nr.: 035008. doi:10.1088/1361-6560/ad1996
Yawson, A. K.; Walter, A.; Wolf, N.; Klüter, S.; Hoegen, P.; Adeberg, S.; Debus, J.; Frank, M.; Jäkel, O.; Giske, K.
2024. Physics in Medicine & Biology, 69 (3), Art.-Nr.: 035008. doi:10.1088/1361-6560/ad1996
Understanding Scalable Perovskite Solar Cell Manufacturing with Explainable AI
Klein, L.; Ziegler, S.; Laufer, F.; Debus, C.; Götz, M.; Maier-Hein, K.; Paetzold, U.; Isensee, F.; Jaeger, P.
2023
Klein, L.; Ziegler, S.; Laufer, F.; Debus, C.; Götz, M.; Maier-Hein, K.; Paetzold, U.; Isensee, F.; Jaeger, P.
2023
Deep-Learning-Based 3-D Surface Reconstruction—A Survey
Farshian, A.; Götz, M.; Cavallaro, G.; Debus, C.; Nießner, M.; Benediktsson, J. A.; Streit, A.
2023. Proceedings of the IEEE, 111 (11), 1464 – 1501. doi:10.1109/JPROC.2023.3321433
Farshian, A.; Götz, M.; Cavallaro, G.; Debus, C.; Nießner, M.; Benediktsson, J. A.; Streit, A.
2023. Proceedings of the IEEE, 111 (11), 1464 – 1501. doi:10.1109/JPROC.2023.3321433
Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI
Klein, L.; Ziegler, S.; Laufer, F.; Debus, C.; Götz, M.; Maier-Hein, K.; Paetzold, U. W.; Isensee, F.; Jäger, P. F.
2024. Advanced Materials, 36 (7), Art.-Nr.: 2307160. doi:10.1002/adma.202307160
Klein, L.; Ziegler, S.; Laufer, F.; Debus, C.; Götz, M.; Maier-Hein, K.; Paetzold, U. W.; Isensee, F.; Jäger, P. F.
2024. Advanced Materials, 36 (7), Art.-Nr.: 2307160. doi:10.1002/adma.202307160
A community‐endorsed open‐source lexicon for contrast agent–based perfusion MRI : A consensus guidelines report from the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI)
on behalf of The Perfusion Study Group of the ISMRM; Dickie, B. R.; Ahmed, Z.; Arvidsson, J.; Bell, L. C.; Buckley, D. L.; Debus, C.; Fedorov, A.; Floca, R.; Gutmann, I.; van der Heijden, R. A.; van Houdt, P. J.; Sourbron, S.; Thrippleton, M. J.; Quarles, C.; Kompan, I. N.
2023. Magnetic Resonance in Medicine. doi:10.1002/mrm.29840
on behalf of The Perfusion Study Group of the ISMRM; Dickie, B. R.; Ahmed, Z.; Arvidsson, J.; Bell, L. C.; Buckley, D. L.; Debus, C.; Fedorov, A.; Floca, R.; Gutmann, I.; van der Heijden, R. A.; van Houdt, P. J.; Sourbron, S.; Thrippleton, M. J.; Quarles, C.; Kompan, I. N.
2023. Magnetic Resonance in Medicine. doi:10.1002/mrm.29840
Project 03: Mathematical Foundations of Bayesian Neural Networks
Debus, C.; Krumscheid, S.
2023. (U. Ehret, M. Frank & KIT-Zentrum MathSEE, Eds.)
Debus, C.; Krumscheid, S.
2023. (U. Ehret, M. Frank & KIT-Zentrum MathSEE, Eds.)
Project 01: Scholars in the loop? Bridging the gap between distinctive knowledge of small disciplines and training data for AI
Götzelmann, G.; Tonne, D.; Debus, C.
2023. (U. Ehret, M. Frank & KIT-Zentrum MathSEE, Eds.)
Götzelmann, G.; Tonne, D.; Debus, C.
2023. (U. Ehret, M. Frank & KIT-Zentrum MathSEE, Eds.)
RNA contact prediction by data efficient deep learning
Taubert, O.; von der Lehr, F.; Bazarova, A.; Faber, C.; Knechtges, P.; Weiel, M.; Debus, C.; Coquelin, D.; Basermann, A.; Streit, A.; Kesselheim, S.; Götz, M.; Schug, A.
2023. Communications Biology, 6 (1), 913. doi:10.1038/s42003-023-05244-9
Taubert, O.; von der Lehr, F.; Bazarova, A.; Faber, C.; Knechtges, P.; Weiel, M.; Debus, C.; Coquelin, D.; Basermann, A.; Streit, A.; Kesselheim, S.; Götz, M.; Schug, A.
2023. Communications Biology, 6 (1), 913. doi:10.1038/s42003-023-05244-9
Massively Parallel Genetic Optimization Through Asynchronous Propagation of Populations
Taubert, O.; Weiel, M.; Coquelin, D.; Farshian, A.; Debus, C.; Schug, A.; Streit, A.; Götz, M.
2023. doi:10.48550/arXiv.2301.08713
Taubert, O.; Weiel, M.; Coquelin, D.; Farshian, A.; Debus, C.; Schug, A.; Streit, A.; Götz, M.
2023. doi:10.48550/arXiv.2301.08713
Massively Parallel Genetic Optimization Through Asynchronous Propagation of Populations
Taubert, O.; Weiel, M.; Coquelin, D.; Farshian, A.; Debus, C.; Schug, A.; Streit, A.; Götz, M.
2023. High Performance Computing – 38th International Conference, ISC High Performance 2023, Hamburg, Germany, May 21–25, 2023, Proceedings. Ed.: A. Bhatele, 106 – 124, Springer Nature Switzerland. doi:10.1007/978-3-031-32041-5_6
Taubert, O.; Weiel, M.; Coquelin, D.; Farshian, A.; Debus, C.; Schug, A.; Streit, A.; Götz, M.
2023. High Performance Computing – 38th International Conference, ISC High Performance 2023, Hamburg, Germany, May 21–25, 2023, Proceedings. Ed.: A. Bhatele, 106 – 124, Springer Nature Switzerland. doi:10.1007/978-3-031-32041-5_6
Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data
Laufer, F.; Ziegler, S.; Schackmar, F.; Viteri, E. A. M.; Götz, M.; Debus, C.; Isensee, F.; Paetzold, U. W.
2023. Solar RRL, 7 (7), Art.-Nr.: 2201114. doi:10.1002/solr.202201114
Laufer, F.; Ziegler, S.; Schackmar, F.; Viteri, E. A. M.; Götz, M.; Debus, C.; Isensee, F.; Paetzold, U. W.
2023. Solar RRL, 7 (7), Art.-Nr.: 2201114. doi:10.1002/solr.202201114
Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads
Caspart, R.; Ziegler, S.; Weyrauch, A.; Obermaier, H.; Raffeiner, S.; Schuhmacher, L. P.; Scholtyssek, J.; Trofimova, D.; Nolden, M.; Reinartz, I.; Isensee, F.; Götz, M.; Debus, C.
2023. High Performance Computing. ISC High Performance 2022 International Workshops – Hamburg, Germany, May 29 – June 2, 2022, Revised Selected Papers. Ed.: H. Anzt, 108–121, Springer International Publishing. doi:10.1007/978-3-031-23220-6_8
Caspart, R.; Ziegler, S.; Weyrauch, A.; Obermaier, H.; Raffeiner, S.; Schuhmacher, L. P.; Scholtyssek, J.; Trofimova, D.; Nolden, M.; Reinartz, I.; Isensee, F.; Götz, M.; Debus, C.
2023. High Performance Computing. ISC High Performance 2022 International Workshops – Hamburg, Germany, May 29 – June 2, 2022, Revised Selected Papers. Ed.: H. Anzt, 108–121, Springer International Publishing. doi:10.1007/978-3-031-23220-6_8
The impact of tumor metabolic activity assessed by
Waltenberger, M.; Furkel, J.; Röhrich, M.; Salome, P.; Debus, C.; Tawk, B.; Gahlawat, A. W.; Kudak, A.; Dostal, M.; Wirkner, U.; Schwager, C.; Herold-Mende, C.; Combs, S. E.; König, L.; Debus, J.; Haberkorn, U.; Abdollahi, A.; Knoll, M.
2022. Frontiers in Oncology, 12, Art.Nr. 901390. doi:10.3389/fonc.2022.901390
Waltenberger, M.; Furkel, J.; Röhrich, M.; Salome, P.; Debus, C.; Tawk, B.; Gahlawat, A. W.; Kudak, A.; Dostal, M.; Wirkner, U.; Schwager, C.; Herold-Mende, C.; Combs, S. E.; König, L.; Debus, J.; Haberkorn, U.; Abdollahi, A.; Knoll, M.
2022. Frontiers in Oncology, 12, Art.Nr. 901390. doi:10.3389/fonc.2022.901390
High-performance data analytics of hybrid rocket fuel combustion data using different machine learning approaches
Debus, C.; Ruettgers, A.; Petrarolo, A.; Kobald, M.; Siggel, M.
2020. AIAA Scitech 2020 Forum, American Institute of Aeronautics and Astronautics. doi:10.2514/6.2020-1161
Debus, C.; Ruettgers, A.; Petrarolo, A.; Kobald, M.; Siggel, M.
2020. AIAA Scitech 2020 Forum, American Institute of Aeronautics and Astronautics. doi:10.2514/6.2020-1161
Impact of fitting algorithms on errors of parameter estimates in dynamic contrast-enhanced MRI
Debus, C.; Floca, R.; Nörenberg, D.; Abdollahi, A.; Ingrisch, M.
2017. Physics in Medicine & Biology, 62 (24), 9322–9340. doi:10.1088/1361-6560/aa8989
Debus, C.; Floca, R.; Nörenberg, D.; Abdollahi, A.; Ingrisch, M.
2017. Physics in Medicine & Biology, 62 (24), 9322–9340. doi:10.1088/1361-6560/aa8989
A point kernel algorithm for microbeam radiation therapy
Debus, C.; Oelfke, U.; Bartzsch, S.
2017. Physics in Medicine & Biology, 62 (21), 8341–8359. doi:10.1088/1361-6560/aa8d63
Debus, C.; Oelfke, U.; Bartzsch, S.
2017. Physics in Medicine & Biology, 62 (21), 8341–8359. doi:10.1088/1361-6560/aa8d63
Feasibility and robustness of dynamic 18F-FET PET based tracer kinetic models applied to patients with recurrent high-grade glioma prior to carbon ion irradiation
Debus, C.; Afshar-Oromieh, A.; Floca, R.; Ingrisch, M.; Knoll, M.; Debus, J.; Haberkorn, U.; Abdollahi, A.
2018. Scientific Reports, 8 (1), Artkl.Nr.: 14760. doi:10.1038/s41598-018-33034-5
Debus, C.; Afshar-Oromieh, A.; Floca, R.; Ingrisch, M.; Knoll, M.; Debus, J.; Haberkorn, U.; Abdollahi, A.
2018. Scientific Reports, 8 (1), Artkl.Nr.: 14760. doi:10.1038/s41598-018-33034-5
Impact of 18F-FET PET on Target Volume Definition and Tumor Progression of Recurrent High Grade Glioma Treated with Carbon-Ion Radiotherapy
Debus, C.; Waltenberger, M.; Floca, R.; Afshar-Oromieh, A.; Bougatf, N.; Adeberg, S.; Heiland, S.; Bendszus, M.; Wick, W.; Rieken, S.; Haberkorn, U.; Debus, J.; Knoll, M.; Abdollahi, A.
2018. Scientific Reports, 8 (1), Artkl.Nr.: 7201. doi:10.1038/s41598-018-25350-7
Debus, C.; Waltenberger, M.; Floca, R.; Afshar-Oromieh, A.; Bougatf, N.; Adeberg, S.; Heiland, S.; Bendszus, M.; Wick, W.; Rieken, S.; Haberkorn, U.; Debus, J.; Knoll, M.; Abdollahi, A.
2018. Scientific Reports, 8 (1), Artkl.Nr.: 7201. doi:10.1038/s41598-018-25350-7
MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging–design, implementation and application on the example of DCE-MRI
Debus, C.; Floca, R.; Ingrisch, M.; Kompan, I.; Maier-Hein, K.; Abdollahi, A.; Nolden, M.
2019. BMC bioinformatics, 20 (1), Artkl.Nr.: 31. doi:10.1186/s12859-018-2588-1
Debus, C.; Floca, R.; Ingrisch, M.; Kompan, I.; Maier-Hein, K.; Abdollahi, A.; Nolden, M.
2019. BMC bioinformatics, 20 (1), Artkl.Nr.: 31. doi:10.1186/s12859-018-2588-1
Accelerating neural network training with distributed asynchronous and selective optimization (DASO)
Coquelin, D.; Debus, C.; Götz, M.; Lehr, F. von der; Kahn, J.; Siggel, M.; Streit, A.
2022. Journal of Big Data, 9 (1), 14. doi:10.1186/s40537-021-00556-1
Coquelin, D.; Debus, C.; Götz, M.; Lehr, F. von der; Kahn, J.; Siggel, M.; Streit, A.
2022. Journal of Big Data, 9 (1), 14. doi:10.1186/s40537-021-00556-1
Heat - A Distributed and Accelerated Tensor Framework for Data Analytics and Machine Learning
Comito, C.; Götz, M.; Debus, C.; Coquelin, D.; Tarnawa, M.; Krajsek, K.; Knechtges, P.; Siggel, M.; Hagemeier, B.; Basermann, A.; Streit, A.
2021, October 5. 1st Artificial Intelligence Symposium on Theory, Application & Research (AI STAR 2021), Online, October 5–6, 2021
Comito, C.; Götz, M.; Debus, C.; Coquelin, D.; Tarnawa, M.; Krajsek, K.; Knechtges, P.; Siggel, M.; Hagemeier, B.; Basermann, A.; Streit, A.
2021, October 5. 1st Artificial Intelligence Symposium on Theory, Application & Research (AI STAR 2021), Online, October 5–6, 2021
Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)
Coquelin, D.; Debus, C.; Götz, M.; Lehr, F. von der; Kahn, J.; Siggel, M.; Streit, A.
2021. Springer. doi:10.21203/rs.3.rs-832355/v1
Coquelin, D.; Debus, C.; Götz, M.; Lehr, F. von der; Kahn, J.; Siggel, M.; Streit, A.
2021. Springer. doi:10.21203/rs.3.rs-832355/v1
HeAT - A Distributed and GPU-accelerated Tensor Framework for Data Analytics
Götz, M.; Debus, C.; Coquelin, D.; Krajsek, K.; Comito, C.; Knechtges, P.; Hagemeier, B.; Tarnawa, M.; Hanselmann, S.; Siggel, M.; Basermann, A.; Streit, A.
2020. 2020 IEEE International Conference on Big Data (Big Data): 10-13 December 2020, online, 276–287, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/BigData50022.2020.9378050
Götz, M.; Debus, C.; Coquelin, D.; Krajsek, K.; Comito, C.; Knechtges, P.; Hagemeier, B.; Tarnawa, M.; Hanselmann, S.; Siggel, M.; Basermann, A.; Streit, A.
2020. 2020 IEEE International Conference on Big Data (Big Data): 10-13 December 2020, online, 276–287, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/BigData50022.2020.9378050
HeAT – a Distributed and GPU-accelerated TensorFramework for Data Analytics
Götz, M.; Debus, C.; Coquelin, D.; Krajsek, K.; Comito, C.; Knechtges, P.; Hagemeier, B.; Tarnawa, M.; Hanselmann, S.; Siggel, M.; Basermann, A.; Streit, A.
2020. 2020 IEEE International Conference on Big Data (Big Data), 276–287, Institute of Electrical and Electronics Engineers (IEEE)
Götz, M.; Debus, C.; Coquelin, D.; Krajsek, K.; Comito, C.; Knechtges, P.; Hagemeier, B.; Tarnawa, M.; Hanselmann, S.; Siggel, M.; Basermann, A.; Streit, A.
2020. 2020 IEEE International Conference on Big Data (Big Data), 276–287, Institute of Electrical and Electronics Engineers (IEEE)
HeAT – a Distributed and GPU-accelerated Tensor Framework for Data Analytics
Götz, M.; Coquelin, D.; Debus, C.; Krajsek, K.; Comito, C.; Knechtges, P.; Hagemeier, B.; Tarnawa, M.; Hanselmann, S.; Siggel, M.; Basermann, A.; Streit, A.
2020. doi:10.5445/IR/1000123473
Götz, M.; Coquelin, D.; Debus, C.; Krajsek, K.; Comito, C.; Knechtges, P.; Hagemeier, B.; Tarnawa, M.; Hanselmann, S.; Siggel, M.; Basermann, A.; Streit, A.
2020. doi:10.5445/IR/1000123473
Courses
| Title | Type | Semester |
|---|---|---|
| Scalable Methods of Artificial Intelligence | Lecture with tutorial (VÜ) | WS 22/23 |
| Scalable Methods of Artificial Intelligence | Lecture with tutorial (VÜ) | WS 21/22 |
Theses
The RAI group offers a number of bachelor and master thesis projects in the topics of scalable and efficient artificial intelligence, quantification of uncertainties, and AI applications in energy informatics. Interested students are welcome to contact us via email.