Kai Kohlhoff

2.5k total citations
22 papers, 1.3k citations indexed

About

Kai Kohlhoff is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Kai Kohlhoff has authored 22 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 5 papers in Computer Vision and Pattern Recognition and 4 papers in Computational Theory and Mathematics. Recurrent topics in Kai Kohlhoff's work include Protein Structure and Dynamics (5 papers), Computational Drug Discovery Methods (4 papers) and Visual Attention and Saliency Detection (3 papers). Kai Kohlhoff is often cited by papers focused on Protein Structure and Dynamics (5 papers), Computational Drug Discovery Methods (4 papers) and Visual Attention and Saliency Detection (3 papers). Kai Kohlhoff collaborates with scholars based in United States, United Kingdom and Italy. Kai Kohlhoff's co-authors include Michele Vendruscolo, Andrea Cavalli, Paul Robustelli, Russ B. Altman, Vijay S. Pande, David E. Konerding, Dan Belov, Xavier Salvatella, Gregory R. Bowman and Diwakar Shukla and has published in prestigious journals such as Journal of the American Chemical Society, Nature Communications and Bioinformatics.

In The Last Decade

Kai Kohlhoff

22 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kai Kohlhoff United States 14 758 227 223 221 162 22 1.3k
Pengyu Hong United States 29 1.3k 1.7× 181 0.8× 44 0.2× 129 0.6× 102 0.6× 100 2.6k
András Lörincz Hungary 21 104 0.1× 128 0.6× 70 0.3× 103 0.5× 112 0.7× 174 1.6k
Clemens Wagner Switzerland 23 684 0.9× 46 0.2× 177 0.8× 33 0.1× 139 0.9× 44 1.1k
Matthieu Chavent France 24 1.2k 1.6× 72 0.3× 102 0.5× 12 0.1× 133 0.8× 52 1.8k
James D. Baker United States 24 746 1.0× 82 0.4× 384 1.7× 22 0.1× 206 1.3× 57 2.1k
R. Jacob Vogelstein United States 23 683 0.9× 64 0.3× 15 0.1× 41 0.2× 423 2.6× 54 2.4k
Yu Shi United States 17 781 1.0× 26 0.1× 55 0.2× 18 0.1× 48 0.3× 49 1.5k
Klaus Obermayer Germany 31 564 0.7× 27 0.1× 27 0.1× 42 0.2× 117 0.7× 159 3.5k
Hideyuki Suzuki Japan 23 752 1.0× 8 0.0× 113 0.5× 67 0.3× 143 0.9× 111 2.2k
John E. Allen United States 8 507 0.7× 32 0.1× 38 0.2× 26 0.1× 16 0.1× 14 882

Countries citing papers authored by Kai Kohlhoff

Since Specialization
Citations

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

Fields of papers citing papers by Kai Kohlhoff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kai Kohlhoff

This figure shows the co-authorship network connecting the top 25 collaborators of Kai Kohlhoff. A scholar is included among the top collaborators of Kai Kohlhoff 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 Kai Kohlhoff. Kai Kohlhoff 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.
He, Junfeng, Qianying Wu, Kai Kohlhoff, et al.. (2024). Smartphone‐based gaze estimation for in‐home autism research. Autism Research. 17(6). 1140–1148. 1 indexed citations
2.
He, Junfeng, Peizhao Li, Jiao Sun, et al.. (2024). Rich Human Feedback for Text-to-Image Generation. 19401–19411. 18 indexed citations
3.
Kohlhoff, Kai, et al.. (2023). Differentially Private Heatmaps. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 7696–7704. 3 indexed citations
4.
Chen, Shi, et al.. (2023). Learning from Unique Perspectives: User-aware Saliency Modeling. 2701–2710. 2 indexed citations
5.
Löhr, Thomas, Kai Kohlhoff, Gabriella T. Heller, Carlo Camilloni, & Michele Vendruscolo. (2021). A kinetic ensemble of the Alzheimer’s Aβ peptide. Nature Computational Science. 1(1). 71–78. 36 indexed citations
6.
Löhr, Thomas, Kai Kohlhoff, Gabriella T. Heller, Carlo Camilloni, & Michele Vendruscolo. (2020). A kinetic ensemble of the Alzheimer's Aβ peptide. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
7.
Valliappan, Nachiappan, Ethan Steinberg, Junfeng He, et al.. (2020). Accelerating eye movement research via accurate and affordable smartphone eye tracking. Nature Communications. 11(1). 4553–4553. 133 indexed citations
8.
Löhr, Thomas, Kai Kohlhoff, Gabriella T. Heller, & Michele Vendruscolo. (2019). Structure and Dynamics of Alzheimer's Associated Amyloid-Beta Peptide. Biophysical Journal. 116(3). 437a–437a. 1 indexed citations
9.
Kohlhoff, Kai, et al.. (2019). Robotic grasp analysis using deformable solid mechanics. Meccanica. 54(11-12). 1767–1784. 9 indexed citations
10.
Kohlhoff, Kai. (2019). Google-Accelerated Biomolecular Simulations. Methods in molecular biology. 15(2). 291–309. 4 indexed citations
11.
Mahler, Jeffrey, Florian T. Pokorny, Brian Hou, et al.. (2016). Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards. 1957–1964. 222 indexed citations
12.
Kohlhoff, Kai, Diwakar Shukla, Morgan Lawrenz, et al.. (2013). Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nature Chemistry. 6(1). 15–21. 330 indexed citations
13.
Hellerstein, Joseph L., Kai Kohlhoff, & David E. Konerding. (2012). Science in the Cloud: Accelerating Discovery in the 21st Century. IEEE Internet Computing. 16(4). 64–68. 16 indexed citations
14.
Kohlhoff, Kai, Vijay S. Pande, & Russ B. Altman. (2012). K-Means for Parallel Architectures Using All-Prefix-Sum Sorting and Updating Steps. IEEE Transactions on Parallel and Distributed Systems. 24(8). 1602–1612. 23 indexed citations
15.
Kohlhoff, Kai, Thomas R. Jahn, David A. Lomas, et al.. (2011). The iFly tracking system for an automated locomotor and behavioural analysis of Drosophila melanogaster. Integrative Biology. 3(7). 755–755. 29 indexed citations
16.
Jahn, Thomas R., Kai Kohlhoff, Michael Scott, et al.. (2011). Detection of early locomotor abnormalities in a Drosophila model of Alzheimer's disease. Journal of Neuroscience Methods. 197(1). 186–189. 32 indexed citations
17.
Kohlhoff, Kai, et al.. (2011). CAMPAIGN: an open-source library of GPU-accelerated data clustering algorithms. Bioinformatics. 27(16). 2321–2322. 17 indexed citations
18.
Robustelli, Paul, Kai Kohlhoff, Andrea Cavalli, & Michele Vendruscolo. (2010). Using NMR Chemical Shifts as Structural Restraints in Molecular Dynamics Simulations of Proteins. Structure. 18(8). 923–933. 121 indexed citations
19.
Kohlhoff, Kai, Paul Robustelli, Andrea Cavalli, Xavier Salvatella, & Michele Vendruscolo. (2009). Fast and Accurate Predictions of Protein NMR Chemical Shifts from Interatomic Distances. Journal of the American Chemical Society. 131(39). 13894–13895. 203 indexed citations
20.
Kannan, Srinivasaraghavan, Kai Kohlhoff, & Martin Zacharias. (2006). B-DNA Under Stress: Over- and Untwisting of DNA during Molecular Dynamics Simulations. Biophysical Journal. 91(8). 2956–2965. 51 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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