Markus Götz

541 total citations
37 papers, 260 citations indexed

About

Markus Götz is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Markus Götz has authored 37 papers receiving a total of 260 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Electrical and Electronic Engineering and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Markus Götz's work include 3D Surveying and Cultural Heritage (4 papers), Scientific Computing and Data Management (4 papers) and Machine Learning in Materials Science (4 papers). Markus Götz is often cited by papers focused on 3D Surveying and Cultural Heritage (4 papers), Scientific Computing and Data Management (4 papers) and Machine Learning in Materials Science (4 papers). Markus Götz collaborates with scholars based in Germany, Iceland and France. Markus Götz's co-authors include Morris Riedel, Achim Streit, Gabriele Cavallaro, Felix Laufer, Ulrich W. Paetzold, J. Kahn, Frank Schultmann, Rebekka Volk, Alexander Schug and Hartwig Anzt and has published in prestigious journals such as Advanced Materials, Nature Communications and Energy & Environmental Science.

In The Last Decade

Markus Götz

32 papers receiving 250 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Markus Götz Germany 10 66 55 52 37 23 37 260
Luana Ruiz United States 7 137 2.1× 32 0.6× 63 1.2× 11 0.3× 11 0.5× 25 251
Xiao Lu China 8 123 1.9× 125 2.3× 128 2.5× 78 2.1× 33 1.4× 10 374
Xiaolong Zhu China 10 63 1.0× 28 0.5× 60 1.2× 22 0.6× 13 0.6× 28 341
Sohil Shah United States 6 85 1.3× 20 0.4× 103 2.0× 7 0.2× 35 1.5× 10 235
Dat Thanh Tran Finland 9 122 1.8× 74 1.3× 70 1.3× 6 0.2× 14 0.6× 16 343
Qizhou Wang China 11 72 1.1× 58 1.1× 26 0.5× 18 0.5× 18 0.8× 35 253
Shengli Song China 9 64 1.0× 24 0.4× 77 1.5× 22 0.6× 40 1.7× 25 335
Zhuolun He Hong Kong 10 92 1.4× 129 2.3× 52 1.0× 11 0.3× 12 0.5× 39 324
Ting Xie China 10 74 1.1× 11 0.2× 42 0.8× 11 0.3× 15 0.7× 39 275
Susanta Chakraborty India 11 66 1.0× 113 2.1× 129 2.5× 14 0.4× 32 1.4× 77 421

Countries citing papers authored by Markus Götz

Since Specialization
Citations

This map shows the geographic impact of Markus Götz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Markus Götz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Götz more than expected).

Fields of papers citing papers by Markus Götz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Markus Götz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Markus Götz. The network helps show where Markus Götz may publish in the future.

Co-authorship network of co-authors of Markus Götz

This figure shows the co-authorship network connecting the top 25 collaborators of Markus Götz. A scholar is included among the top collaborators of Markus Götz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Markus Götz. Markus Götz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Götz, Markus, et al.. (2025). AutoPQ: Automating quantile estimation from point forecasts in the context of sustainability. Applied Energy. 392. 125931–125931. 1 indexed citations
2.
Laufer, Felix, Markus Götz, & Ulrich W. Paetzold. (2025). Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication. Energy & Environmental Science. 18(4). 1767–1782. 5 indexed citations
3.
Quinting, Julian, et al.. (2024). Architectural insights into and training methodology optimization of Pangu-Weather. Geoscientific model development. 17(23). 8873–8884.
4.
Götz, Markus, et al.. (2024). Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing. Nature Communications. 15(1). 6997–6997. 7 indexed citations
5.
Kahn, J., et al.. (2024). Organising AI for safety: Identifying structural vulnerabilities to guide the design of AI-enhanced socio-technical systems. Safety Science. 184. 106731–106731. 1 indexed citations
6.
7.
Streit, Achim, et al.. (2024). ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers. elib (German Aerospace Center). 1187–1194.
8.
Laufer, Felix, Sebastian Ziegler, Fabian Schackmar, et al.. (2023). Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data. Solar RRL. 7(7). 18 indexed citations
9.
Ziegler, Sebastian, Felix Laufer, Markus Götz, et al.. (2023). Discovering Process Dynamics for Scalable Perovskite Solar Cell Manufacturing with Explainable AI. Advanced Materials. 36(7). e2307160–e2307160. 15 indexed citations
10.
Piraud, Marie, Andrés Camero, Markus Götz, et al.. (2023). Providing AI expertise as an infrastructure in academia. Patterns. 4(8). 100819–100819.
11.
Kahn, J., Markus Götz, Yu Hou, et al.. (2023). Thermal Bridges on Building Rooftops. Scientific Data. 10(1). 4 indexed citations
12.
Bazarova, Alina, Achim Basermann, Achim Streit, et al.. (2023). RNA contact prediction by data efficient deep learning. Communications Biology. 6(1). 913–913. 3 indexed citations
13.
Piraud, Marie, et al.. (2023). Reporting electricity consumption is essential for sustainable AI. Nature Machine Intelligence. 5(11). 1176–1178. 22 indexed citations
14.
Götz, Markus, et al.. (2022). Accelerating neural network training with distributed asynchronous and selective optimization (DASO). Journal Of Big Data. 9(1). 5 indexed citations
15.
Kahn, J., et al.. (2022). Deep learning approaches to building rooftop thermal bridge detection from aerial images. Automation in Construction. 146. 104690–104690. 22 indexed citations
16.
Kahn, J., G. Dujany, P. Goldenzweig, et al.. (2022). Learning tree structures from leaves for particle decay reconstruction. Machine Learning Science and Technology. 3(3). 35012–35012. 5 indexed citations
17.
Götz, Markus, et al.. (2021). Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions. Nature Machine Intelligence. 3(8). 727–734. 24 indexed citations
18.
Götz, Markus, et al.. (2020). HeAT – a Distributed and GPU-accelerated Tensor Framework for Data Analytics. Repository KITopen (Karlsruhe Institute of Technology). 276–287. 4 indexed citations
19.
Götz, Markus, Philipp Wortmann, Sonja Schmid, & Thorsten Hugel. (2018). Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions. Journal of Visualized Experiments. 2 indexed citations
20.
Götz, Markus, et al.. (2016). Automatic Object Detection Using DBSCAN for Counting Intoxicated Flies in the FLORIDA Assay. 746–751. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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