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Steinbuch Centre for Computing (SCC)
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    Steinbuch Centre for Computing (SCC)

     

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    Dr.  Charlotte Debus

    Dr. Charlotte Debus

    • Junior Research Group Leader
    • E-Mail charlotte debus ∂does-not-exist.kit edu
    • Data Analytics, Access and Applications
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    since 2022Junior Research Group LeaderSteinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
    2020 - 2022Research AssociateSteinbuch Centre for Computing (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


    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 18F-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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