Lars Rosenbaum

2.5k total citations · 1 hit paper
16 papers, 1.4k citations indexed

About

Lars Rosenbaum is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Lars Rosenbaum has authored 16 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 6 papers in Materials Chemistry. Recurrent topics in Lars Rosenbaum's work include Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (6 papers) and Metabolomics and Mass Spectrometry Studies (5 papers). Lars Rosenbaum is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (6 papers) and Metabolomics and Mass Spectrometry Studies (5 papers). Lars Rosenbaum collaborates with scholars based in Germany, United States and Sweden. Lars Rosenbaum's co-authors include Klaus Dietmayer, Di Feng, Fabian Timm, Claudius Gläser, Heinz Hertlein, W. Wiesbeck, Christian Schütz, Andreas Zell, Georg Hinselmann and Andreas Jahn and has published in prestigious journals such as Clinical Chemistry, IEEE Transactions on Intelligent Transportation Systems and Journal of Chemical Information and Modeling.

In The Last Decade

Lars Rosenbaum

16 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

2020 864 citations Di Feng, Christian Schütz et al. IEEE Transactions on Intelligent Transportation Systems profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lars Rosenbaum Germany	12	610	319	278	215	214	16	1.4k
Jianren Wang United States	8	645 1.1×	209 0.7×	285 1.0×	125 0.6×	174 0.8×	26	1.3k
Zhi Yan China	21	652 1.1×	83 0.3×	207 0.7×	199 0.9×	388 1.8×	93	1.7k
Norbert Stoll Germany	19	280 0.5×	106 0.3×	290 1.0×	29 0.1×	204 1.0×	123	1.4k
Wenming Cao China	23	850 1.4×	68 0.2×	475 1.7×	36 0.2×	337 1.6×	185	2.3k
Mohamed Nounou Qatar	32	133 0.2×	185 0.6×	571 2.1×	129 0.6×	85 0.4×	228	3.4k
Chen Yang China	20	460 0.8×	147 0.5×	294 1.1×	22 0.1×	74 0.3×	86	1.5k
Silong Peng China	26	1.0k 1.7×	162 0.5×	195 0.7×	25 0.1×	168 0.8×	164	2.4k
Peng Chen China	25	964 1.6×	145 0.5×	261 0.9×	44 0.2×	424 2.0×	195	2.2k

Countries citing papers authored by Lars Rosenbaum

Since Specialization

Citations

This map shows the geographic impact of Lars Rosenbaum'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 Lars Rosenbaum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lars Rosenbaum more than expected).

Fields of papers citing papers by Lars Rosenbaum

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lars Rosenbaum. 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 Lars Rosenbaum. The network helps show where Lars Rosenbaum may publish in the future.

Co-authorship network of co-authors of Lars Rosenbaum

This figure shows the co-authorship network connecting the top 25 collaborators of Lars Rosenbaum. A scholar is included among the top collaborators of Lars Rosenbaum 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 Lars Rosenbaum. Lars Rosenbaum is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Feng, Di, et al.. (2022). DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 560–567. 22 indexed citations

Feng, Di, Zining Wang, Yiyang Zhou, et al.. (2021). Labels are Not Perfect: Inferring Spatial Uncertainty in Object Detection. IEEE Transactions on Intelligent Transportation Systems. 23(8). 9981–9994. 17 indexed citations

Feng, Di, Yiyang Zhou, Lars Rosenbaum, et al.. (2020). Inferring Spatial Uncertainty in Object Detection. 5792–5799. 16 indexed citations

Feng, Di, Christian Schütz, Lars Rosenbaum, et al.. (2020). Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges. IEEE Transactions on Intelligent Transportation Systems. 22(3). 1341–1360. 864 indexed citations breakdown →

Feng, Di, Wei Xiao, Lars Rosenbaum, Atsuto Maki, & Klaus Dietmayer. (2019). Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector. 667–674. 50 indexed citations

Rosenbaum, Lars, et al.. (2015). A ranking method for the concurrent learning of compounds with various activity profiles. Journal of Cheminformatics. 7(1). 13 indexed citations

Eichner, Johannes, Lars Rosenbaum, Clemens Wrzodek, et al.. (2014). Integrated enrichment analysis and pathway-centered visualization of metabolomics, proteomics, transcriptomics, and genomics data by using the InCroMAP software. Journal of Chromatography B. 966. 77–82. 34 indexed citations

Rosenbaum, Lars, et al.. (2013). Inferring multi-target QSAR models with taxonomy-based multi-task learning. Journal of Cheminformatics. 5(1). 33–33. 33 indexed citations

Yin, Peiyuan, Andreas Peter, Holger Franken, et al.. (2013). Preanalytical Aspects and Sample Quality Assessment in Metabolomics Studies of Human Blood. Clinical Chemistry. 59(5). 833–845. 216 indexed citations

10.

Lucio, Marianna, Sara Forcisi, Silke S. Heinzmann, et al.. (2012). „Metabolomics“ in der Diabetesforschung. Der Diabetologe. 8(1). 42–48. 3 indexed citations

11.

Hinselmann, Georg, Lars Rosenbaum, Andreas Jahn, Nikolas Fechner, & Andreas Zell. (2011). jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints. Journal of Cheminformatics. 3(1). 3–3. 69 indexed citations

12.

Rosenbaum, Lars, Georg Hinselmann, Andreas Jahn, & Andreas Zell. (2011). Interpreting linear support vector machine models with heat map molecule coloring. Journal of Cheminformatics. 3(1). 11–11. 44 indexed citations

13.

Hinselmann, Georg, Lars Rosenbaum, Andreas Jahn, et al.. (2011). Large-Scale Learning of Structure−Activity Relationships Using a Linear Support Vector Machine and Problem-Specific Metrics. Journal of Chemical Information and Modeling. 51(2). 203–213. 27 indexed citations

14.

Jahn, Andreas, Georg Hinselmann, Lars Rosenbaum, Nikolas Fechner, & Andreas Zell. (2011). Boltzmann‐Enhanced Flexible Atom‐Pair Kernel with Dynamic Dimension Reduction. Molecular Informatics. 30(4). 307–315. 1 indexed citations

15.

Jahn, Andreas, Lars Rosenbaum, Georg Hinselmann, & Andreas Zell. (2011). 4D Flexible Atom-Pairs: An efficient probabilistic conformational space comparison for ligand-based virtual screening. Journal of Cheminformatics. 3(1). 23–23. 3 indexed citations

16.

Fechner, Nikolas, Georg Hinselmann, Andreas Jahn, Lars Rosenbaum, & Andreas Zell. (2010). A Free–Wilson‐like Approach to Analyze QSAR Models Based on Graph Decomposition Kernels. Molecular Informatics. 29(6-7). 491–497. 2 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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