Dr. Markus Götz
- KI Ingenieuswesen
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449
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342
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+49 721 608-29334
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markus goetz ∂does-not-exist.kit edu
- 01/2018 –
Wissenschaftlicher Mitarbeiter Steinbuch Centre for Computing (SCC), Karlsruher Institut für Technologie (KIT) - 04/2014 – 12/2017
Promotion in Computational Engineering, School of Engineering and Natural Sciences (SENS), Universität Island und Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich (FZJ) - 09/2010 – 03/2014
Master of Science in IT-System Engineering, Hasso-Plattner-Institut (HPI), Universität Potsdam - 06/2011 – 09/2011
Europäische Organisation für Kernforschung (CERN), Genf, Schweiz - 08/2010 – 06/2011
ERASMUS Blekinge Tekniska Högskola (BTH), Karlskrona, Schweden - 09/2007 – 07/2010
Bachelor of Science in IT-Systems Engineering, Hasso-Plattner-Institut (HPI), Universität Potsdam
Publikationsliste KITopen
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 AG. 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 AG. doi:10.1007/978-3-031-32041-5_6
Thermal Bridges on Building Rooftops
Mayer, Z.; Kahn, J.; Götz, M.; Hou, Y.; Beiersdörfer, T.; Blumenröhr, N.; Volk, R.; Streit, A.; Schultmann, F.
2023. Scientific Data, 10 (1), Art.-Nr.: 268. doi:10.1038/s41597-023-02140-z
Mayer, Z.; Kahn, J.; Götz, M.; Hou, Y.; Beiersdörfer, T.; Blumenröhr, N.; Volk, R.; Streit, A.; Schultmann, F.
2023. Scientific Data, 10 (1), Art.-Nr.: 268. doi:10.1038/s41597-023-02140-z
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, 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, Art.-Nr.: 2201114. doi:10.1002/solr.202201114
Prediction of Optimal Solvers for Sparse Linear Systems Using Deep Learning
Funk, Y.; Götz, M.; Anzt, H.
2022. Proceedings of the 2022 SIAM Conference on Parallel Processing for Scientific Computing (PP). Ed.: X. Li, 14–24, Society for Industrial and Applied Mathematics (SIAM). doi:10.1137/1.9781611977141.2
Funk, Y.; Götz, M.; Anzt, H.
2022. Proceedings of the 2022 SIAM Conference on Parallel Processing for Scientific Computing (PP). Ed.: X. Li, 14–24, Society for Industrial and Applied Mathematics (SIAM). doi:10.1137/1.9781611977141.2
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
Lowest Common Ancestor Generations (LCAG) Phasespace Particle Decay Reconstruction Dataset
Kahn, J.; Taubert, O.; Tsaklidis, I.; Reuter, L.; Dujany, G.; Boeckh, T.; Thaller, A.; Goldenzweig, P.; Bernlochner, F.; Streit, A.; Götz, M.
2022, August 11. doi:10.5281/zenodo.6983258
Kahn, J.; Taubert, O.; Tsaklidis, I.; Reuter, L.; Dujany, G.; Boeckh, T.; Thaller, A.; Goldenzweig, P.; Bernlochner, F.; Streit, A.; Götz, M.
2022, August 11. doi:10.5281/zenodo.6983258
Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset
Kahn, J.; Mayer, Z.; Hou, Y.; Beiersdörfer, T.; Götz, M.; Volk, R.
2022, November 25. doi:10.5281/zenodo.7360996
Kahn, J.; Mayer, Z.; Hou, Y.; Beiersdörfer, T.; Götz, M.; Volk, R.
2022, November 25. doi:10.5281/zenodo.7360996
Deep learning approaches to building rooftop thermal bridge detection from aerial images
Mayer, Z.; Kahn, J.; Hou, Y.; Götz, M.; Volk, R.; Schultmann, F.
2023. Automation in Construction, 146, Art.-Nr.: 104690. doi:10.1016/j.autcon.2022.104690
Mayer, Z.; Kahn, J.; Hou, Y.; Götz, M.; Volk, R.; Schultmann, F.
2023. Automation in Construction, 146, Art.-Nr.: 104690. doi:10.1016/j.autcon.2022.104690
A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure
Schilling, M.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2022. Proceedings of the 7th bwHPC Symposium, 69–74, Kommunikations- und Informationszentrum (kiz). doi:10.18725/OPARU-46069
Schilling, M.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2022. Proceedings of the 7th bwHPC Symposium, 69–74, Kommunikations- und Informationszentrum (kiz). doi:10.18725/OPARU-46069
Learning tree structures from leaves for particle decay reconstruction
Kahn, J.; Tsaklidis, I.; Taubert, O.; Reuter, L.; Dujany, G.; Boeckh, T.; Thaller, A.; Goldenzweig, P.; Bernlochner, F.; Streit, A.; Götz, M.
2022. Machine Learning: Science and Technology, 3 (3), Art.Nr. 035012. doi:10.1088/2632-2153/ac8de0
Kahn, J.; Tsaklidis, I.; Taubert, O.; Reuter, L.; Dujany, G.; Boeckh, T.; Thaller, A.; Goldenzweig, P.; Bernlochner, F.; Streit, A.; Götz, M.
2022. Machine Learning: Science and Technology, 3 (3), Art.Nr. 035012. doi:10.1088/2632-2153/ac8de0
Thermal Bridges on Building Rooftops - Hyperspectral (RGB + Thermal + Height) drone images of Karlsruhe, Germany, with thermal bridge annotations
Mayer, Z.; Kahn, J.; Hou, Y.; Beiersdörfer, T.; Götz, M.; Volk, R.
2022, Mai 11. doi:10.5281/zenodo.6517768
Mayer, Z.; Kahn, J.; Hou, Y.; Beiersdörfer, T.; Götz, M.; Volk, R.
2022, Mai 11. doi:10.5281/zenodo.6517768
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, Oktober 5. 1st Artificial Intelligence Symposium on Theory, Application & Research (AI STAR 2021), Online, 5.–6. Oktober 2021
Comito, C.; Götz, M.; Debus, C.; Coquelin, D.; Tarnawa, M.; Krajsek, K.; Knechtges, P.; Siggel, M.; Hagemeier, B.; Basermann, A.; Streit, A.
2021, Oktober 5. 1st Artificial Intelligence Symposium on Theory, Application & Research (AI STAR 2021), Online, 5.–6. Oktober 2021
Evolutionary Optimization of Neural Architectures in Remote Sensing Classification Problems
Coquelin, D.; Sedona, R.; Riedel, M.; Götz, M.
2021. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 12-16 July 2021, 1587–1590, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IGARSS47720.2021.9554309
Coquelin, D.; Sedona, R.; Riedel, M.; Götz, M.
2021. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 12-16 July 2021, 1587–1590, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/IGARSS47720.2021.9554309
A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure
Schilling, M. P.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2021, November 8. 7th bwHPC Symposium (2021), Online, 8. November 2021
Schilling, M. P.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2021, November 8. 7th bwHPC Symposium (2021), Online, 8. November 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
Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions
Weiel, M.; Götz, M.; Klein, A.; Coquelin, D.; Floca, R.; Schug, A.
2021. Nature machine intelligence, 3 (8), 727–734. doi:10.1038/s42256-021-00366-3
Weiel, M.; Götz, M.; Klein, A.; Coquelin, D.; Floca, R.; Schug, A.
2021. Nature machine intelligence, 3 (8), 727–734. doi:10.1038/s42256-021-00366-3
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
Loss Scheduling for Class-Imbalanced Segmentation Problems
Taubert, O.; Götz, M.; Schug, A.; Streit, A.
2020. 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 422–427, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICMLA51294.2020.00073
Taubert, O.; Götz, M.; Schug, A.; Streit, A.
2020. 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 422–427, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICMLA51294.2020.00073
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
Machine learning-aided numerical linear Algebra: Convolutional neural networks for the efficient preconditioner generation
Götz, M.; Anzt, H.
2019. Proceedings of ScalA 2018: 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, 49–56, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ScalA.2018.00010
Götz, M.; Anzt, H.
2019. Proceedings of ScalA 2018: 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, 49–56, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ScalA.2018.00010
Remote Sensing Data Analytics with the Udocker Container Tool using Multi-GPU Deep Learning Systems
Cavallaro, G.; Kozlov, V.; Götz, M.; Riedel, M.
2019. Proceedings of 2019 Big Data from Space (BiDS’19). Ed.: S. Pierre, 177–180. doi:10.2760/848593
Cavallaro, G.; Kozlov, V.; Götz, M.; Riedel, M.
2019. Proceedings of 2019 Big Data from Space (BiDS’19). Ed.: S. Pierre, 177–180. doi:10.2760/848593
The Helmholtz Analytics Toolkit (HEAT): A scientific Big Data Library for HPC
Krajsek, K.; Comito, C.; Götz, M.; Hagemeier, B.; Knechtges, P.; Siggel, M.
2018. Proceedings of the Extreme Data Workshop 2018
Krajsek, K.; Comito, C.; Götz, M.; Hagemeier, B.; Knechtges, P.; Siggel, M.
2018. Proceedings of the Extreme Data Workshop 2018
Machine learning-aided numerical linear algebra: Convolutional neural network for the efficient preconditioner generation
Götz, M.; Anzt, H.
2018. ScalA18: 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Dallas, TX, November 12, 2018
Götz, M.; Anzt, H.
2018. ScalA18: 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, Dallas, TX, November 12, 2018
Abschlussarbeiten
Motivierte Studierende können sich aus einer Reihe von Bachelor- und Masterarbeitsthemen in den Themenbereichen: künstlicher Intelligenz, Hochleistungsrechnen und Anwendungen in der Energieinformatik wählen.
Weitere Informationen können per E-Mail erfragt werden.
- 2019 –
Helmholtz Artificial Intelligence Cooperation Unit (Helmholtz AI) - 2020 –
Protein Folding by Learning (ProFiLe) - 2017 – 2021
Helmholtz Analytics Framework (HAF) - 2012 – 2016
Large-Scale Data Management and Analysis (LSDMA)