Benjamin Säfken

996 total citations · 1 hit paper
23 papers, 567 citations indexed

About

Benjamin Säfken is a scholar working on Artificial Intelligence, Statistics and Probability and Management Science and Operations Research. According to data from OpenAlex, Benjamin Säfken has authored 23 papers receiving a total of 567 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 7 papers in Statistics and Probability and 5 papers in Management Science and Operations Research. Recurrent topics in Benjamin Säfken's work include Statistical Methods and Bayesian Inference (6 papers), Statistical Methods and Inference (6 papers) and Topic Modeling (5 papers). Benjamin Säfken is often cited by papers focused on Statistical Methods and Bayesian Inference (6 papers), Statistical Methods and Inference (6 papers) and Topic Modeling (5 papers). Benjamin Säfken collaborates with scholars based in Germany, United Kingdom and Kazakhstan. Benjamin Säfken's co-authors include Simon N. Wood, Natalya Pya, Thomas Kneib, Sonja Greven, David Rügamer, Astrid Krenz, André Python, Jana Lasser, Krisztina Kis‐Katos and Manish Kumar and has published in prestigious journals such as Journal of the American Statistical Association, Biometrika and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Benjamin Säfken

19 papers receiving 551 citations

Hit Papers

Smoothing Parameter and Model Selection for General Smoot... 2017 2026 2020 2023 2017 100 200 300
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.

  1. Smoothing Parameter and Model Selection for General Smooth Models
    2017 380 citations Simon N. Wood, Natalya Pya et al. Bristol Research (University of Bristol) profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Benjamin Säfken Germany 8 103 97 90 73 55 23 567
Natalya Pya Kazakhstan 5 118 1.1× 127 1.3× 63 0.7× 105 1.4× 81 1.5× 8 611
Matteo Fasiolo United Kingdom 11 130 1.3× 69 0.7× 95 1.1× 39 0.5× 42 0.8× 23 505
Steffen Möritz Germany 3 80 0.8× 126 1.3× 73 0.8× 29 0.4× 35 0.6× 4 628
Mikis D. Stasinopoulos United Kingdom 8 55 0.5× 100 1.0× 70 0.8× 150 2.1× 58 1.1× 14 724
Jinting Zhang China 12 68 0.7× 187 1.9× 66 0.7× 120 1.6× 22 0.4× 29 546
JJ Allaire United States 9 65 0.6× 51 0.5× 104 1.2× 53 0.7× 21 0.4× 26 685
Vyacheslav Lyubchich United States 16 127 1.2× 151 1.6× 53 0.6× 28 0.4× 65 1.2× 47 602
Winston Chang United States 9 96 0.9× 59 0.6× 59 0.7× 30 0.4× 45 0.8× 16 609
Robin K. S. Hankin New Zealand 18 90 0.9× 197 2.0× 117 1.3× 102 1.4× 53 1.0× 56 923
Fernanda De Bastiani Brazil 11 83 0.8× 113 1.2× 80 0.9× 191 2.6× 50 0.9× 34 798

Countries citing papers authored by Benjamin Säfken

Since Specialization
Citations

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

Fields of papers citing papers by Benjamin Säfken

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Benjamin Säfken

This figure shows the co-authorship network connecting the top 25 collaborators of Benjamin Säfken. A scholar is included among the top collaborators of Benjamin Säfken 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 Benjamin Säfken. Benjamin Säfken 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.
Säfken, Benjamin, et al.. (2025). Gradient-based smoothing parameter estimation for neural P-splines. Computational Statistics. 40(7). 3645–3663.
2.
Kneib, Thomas, et al.. (2025). Enhancing Adaptive Spline Regression: An Evolutionary Approach to Optimal Knot Placement and Smoothing Parameter Selection. Journal of Computational and Graphical Statistics. 34(4). 1397–1409.
3.
4.
Säfken, Benjamin, et al.. (2025). Probabilistic Topic Modeling With Transformer Representations. IEEE Transactions on Neural Networks and Learning Systems. 36(8). 14551–14565. 2 indexed citations
5.
Säfken, Benjamin, et al.. (2024). Topics in the Haystack: Enhancing Topic Quality through Corpus Expansion. Computational Linguistics. 50(2). 619–655. 4 indexed citations
6.
7.
Säfken, Benjamin, et al.. (2024). One-way ticket to the moon? An NLP-based insight on the phenomenon of small-scale neo-broker trading. Social Network Analysis and Mining. 14(1). 1 indexed citations
8.
Säfken, Benjamin & David Rügamer. (2024). Editorial special issue: Bridging the gap between AI and Statistics. AStA Advances in Statistical Analysis. 108(2). 225–229. 1 indexed citations
9.
Säfken, Benjamin, Thomas Kneib, & Simon N. Wood. (2024). On the degrees of freedom of the smoothing parameter. Biometrika. 112(1). 2 indexed citations
10.
Kneib, Thomas, et al.. (2023). Coherence based Document Clustering. GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen). 9–16. 6 indexed citations
11.
Kis‐Katos, Krisztina, et al.. (2022). An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter. International Journal of Data Science and Analytics. 20(2). 269–289. 6 indexed citations
12.
Python, André, et al.. (2022). Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data. Computational Statistics. 38(2). 647–674. 19 indexed citations
13.
Kneib, Thomas, et al.. (2021). Rage Against the Mean – A Review of Distributional Regression Approaches. Econometrics and Statistics. 26. 99–123. 49 indexed citations
14.
Säfken, Benjamin, et al.. (2021). AuDoLab: Automatic document labelling and classification for extremely unbalanced data. The Journal of Open Source Software. 6(66). 3719–3719. 1 indexed citations
15.
Säfken, Benjamin, et al.. (2021). Stock Price Predictions with LSTM Neural Networks and Twitter Sentiment. Statistics Optimization & Information Computing. 9(2). 268–287. 19 indexed citations
16.
Säfken, Benjamin, et al.. (2021). Model averaging for linear mixed models via augmented Lagrangian. Computational Statistics & Data Analysis. 167. 107351–107351. 2 indexed citations
17.
Lasser, Jana, et al.. (2020). Introductory data science across disciplines, using Python, case studies, and industry consulting projects. Teaching Statistics. 43(S1). 9 indexed citations
18.
Säfken, Benjamin, et al.. (2020). TTLocVis: A Twitter Topic Location Visualization Package. The Journal of Open Source Software. 5(54). 2507–2507. 11 indexed citations
19.
Wood, Simon N., Natalya Pya, & Benjamin Säfken. (2017). Smoothing Parameter and Model Selection for General Smooth Models. Bristol Research (University of Bristol). 380 indexed citations breakdown →
20.
Wood, Simon N., Natalya Pya, & Benjamin Säfken. (2016). Rejoinder. Journal of the American Statistical Association. 111(516). 1573–1575. 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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