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Scientific Computing Center (SCC)
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              • Mass storage for scientific data (LSDF Online Storage)
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            • Research and develop
            Research and develop
            • High Performance Computing
              • High Performance Computing
              High Performance Computing
              • High Performance Computing (HPC) and Clustercomputing
                • High Performance Computing (HPC) and Clustercomputing
                High Performance Computing (HPC) and Clustercomputing
                • HoreKa
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              • Computer algebra system (CAS)
              • Technical scientific applications
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            • Data (storage, sharing, management)
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              • Anonymous ftp server
              • MySQL database
              • MS SQL database
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              • PostgreSQL Datenbanken
              • Backup
              • KIT data storage (OU directory)
              • Serviceteam Research Data Management
              • bwDataArchive - longterm archive for scientific data
              • Mass storage for scientific data (LSDF Online Storage)
              • Data share and online storage for desktop data (bwSync&Share)
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              • Websites (homepage, domain)
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              • Computer algebra system (CAS)
              • Technical scientific application
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              • Websites (homepage, domain)
              Websites (homepage, domain)
              • Web Content Management
              • Internet sites for students
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              • Central printing and plotting service
              • COPS
              • Print account
              • Klausurdruck
              • Lamination
              • Pool-Drucker
              • Scanner
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            • Data (storage, sharing, management)
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              Data (storage, sharing, management)
              • KIT data storage (for Students)
              • Data share and online storage for desktop data (bwSync&Share)
              • Mass storage for scientific data (LSDF Online Storage)
              • Anonymous ftp server
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              • Backup
              • Serviceteam Research Data Management
              • MS SQL database
              • MySQL database
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              • KIT data storage (personal directory)
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              IT working environment and mobile devices
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              • Die kleine Baumschule
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                • Computational and Mathematical Modeling Program
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                  Computational and Mathematical Modeling Program
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                      • Grundlagen Künstlicher Intelligenz und ihre Anwendung in allen Schulfächern
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          Scientific Computing Center (SCC)

           

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          Dr.  Charlotte Debus

          Dr. Charlotte Debus

          • Junior Research Group Leader
          • E-Mail charlotte debus ∂does-not-exist.kit edu
          • Junior Research Group Robust and Efficient AI
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          since 2022Junior Research Group LeaderScientific Computing Center (SCC), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
          2020 - 2022Research AssociateScientific Computing Center (SCC), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
          2019 - 2020Research AssociateInstitute of Software Technology, German Aerospace Center (DLR), Cologne, Germany
          2016 - 2019Research associate, project group leaderDepartment of Translational Radiooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
          2013 - 2016PhD PhysicsUniversity of Heidelberg
          2013TraineeEuropean Synchrotron Radiation Facility (ESRF), Grenoble, France
          2010 - 2012Master of Science PhysicsUniversity of Heidelberg
          2011Semester abroadUniversidade de Lisboa, Portugal
          2007 - 2010Bachelor of Science PhysicsUniversity 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, 18, 10674–10683. 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
          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
          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
          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
          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
          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
          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
          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
          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
          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
          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
          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
          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
          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
          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
          Project 03: Mathematical Foundations of Bayesian Neural Networks
          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.)
          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
          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
          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
          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
          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
          The impact of tumor metabolic activity assessed by F-FET amino acid PET imaging in particle radiotherapy of high-grade glioma patients
          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
          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
          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
          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
          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
          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
          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
          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
          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
          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
          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)
          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

          Courses


          TitleTypeSemester
          Scalable Methods of Artificial IntelligenceLecture with tutorial (VÜ)WS 22/23
          Scalable Methods of Artificial IntelligenceLecture 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.

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          last change: 2024-01-25
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