Jakob Richter

2.5k total citations · 2 hit papers
19 papers, 1.3k citations indexed

About

Jakob Richter is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Surgery. According to data from OpenAlex, Jakob Richter has authored 19 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Computational Theory and Mathematics and 2 papers in Surgery. Recurrent topics in Jakob Richter's work include Machine Learning and Data Classification (6 papers), Advanced Multi-Objective Optimization Algorithms (4 papers) and Reproductive Biology and Fertility (2 papers). Jakob Richter is often cited by papers focused on Machine Learning and Data Classification (6 papers), Advanced Multi-Objective Optimization Algorithms (4 papers) and Reproductive Biology and Fertility (2 papers). Jakob Richter collaborates with scholars based in Germany, United States and Spain. Jakob Richter's co-authors include Patrick Schratz, Michel Lang, Bernd Bischl, Jannes Muenchow, Eugenia Iturritxa, Alexander Brenning, Stefan Coors, Martin Binder, Lars Kotthoff and Giuseppe Casalicchio and has published in prestigious journals such as Bioinformatics, Machine Learning and Ecological Modelling.

In The Last Decade

Jakob Richter

18 papers receiving 1.3k 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.

Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

2023 398 citations Bernd Bischl, Martin Binder et al. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery profile →
Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data

2019 367 citations Patrick Schratz, Jannes Muenchow et al. Ecological Modelling profile →

Peers

Jakob Richter

ECOLO

GPC

Laura Toloşi Germany

Jaime L. Speiser United States

Silke Janitza Germany

Jing Xiao China

Bertrand Michel France

Jorge Martínez Sánchez Spain

Ribana Roscher Germany

Francisco F. Rivera Spain

Juan Diego Rodríguez Spain

Xing Wang China

Laura Toloşi Germany

Jakob Richter

391 ×1.3

144 ×0.9

133 ×1.0

ECOLO

124 ×0.9

GPC

258 ×2.2

Citations per year, relative to Jakob Richter Jakob Richter (= 1×) peers Laura Toloşi

Countries citing papers authored by Jakob Richter

Since Specialization

Citations

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

Fields of papers citing papers by Jakob Richter

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jakob Richter

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

All Works

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19 of 19 papers shown

Menon, Karthik, et al.. (2025). svZeroDSolver: A modular package for lumped-parameter cardiovascular simulations. The Journal of Open Source Software. 10(109). 7595–7595.

Richter, Jakob, Karthik Menon, Wolfgang A. Wall, et al.. (2025). Bayesian Windkessel calibration using optimized zero-dimensional surrogate models. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 383(2292). 20240223–20240223. 4 indexed citations

Menon, Karthik, Jakob Richter, Patricia K. Nguyen, et al.. (2024). Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees. PubMed. 2(1). 9–9. 6 indexed citations

Lee, John D., Jakob Richter, Martin R. Pfaller, et al.. (2024). A probabilistic neural twin for treatment planning in peripheral pulmonary artery stenosis. International Journal for Numerical Methods in Biomedical Engineering. 40(5). e3820–e3820. 5 indexed citations

Pielok, Tobias, Florian Pfisterer, Stefan Coors, et al.. (2023). Multi-Objective Hyperparameter Optimization in Machine Learning—An Overview. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 3(4). 1–50. 36 indexed citations

Bischl, Bernd, Martin Binder, Michel Lang, et al.. (2023). Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 13(2). 398 indexed citations breakdown →

Richter, Jakob, Tim Friede, & Jörg Rahnenführer. (2022). Improving adaptive seamless designs through Bayesian optimization. Biometrical Journal. 64(5). 948–963. 5 indexed citations

Bian, Jiang, Jakob Richter, Kuan-Hsun Chen, et al.. (2021). MODES: model-based optimization on distributed embedded systems. Machine Learning. 110(6). 1527–1547. 5 indexed citations

Richter, Jakob, et al.. (2020). Model-based optimization with concept drifts. 877–885. 3 indexed citations

10.

Lang, Michel, et al.. (2020). Define and Work with Parameter Spaces for Complex Algorithms [R package paradox version 0.6.0]. 1 indexed citations

11.

Lang, Michel, Martin Binder, Jakob Richter, et al.. (2019). mlr3: A modern object-oriented machine learning framework in R. The Journal of Open Source Software. 4(44). 1903–1903. 235 indexed citations

12.

Schratz, Patrick, Jannes Muenchow, Eugenia Iturritxa, Jakob Richter, & Alexander Brenning. (2019). Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. Ecological Modelling. 406. 109–120. 367 indexed citations breakdown →

13.

Richter, Jakob, Katrin Madjar, & Jörg Rahnenführer. (2019). Model-based optimization of subgroup weights for survival analysis. Bioinformatics. 35(14). i484–i491. 3 indexed citations

14.

Richter, Jakob, Bernd Bischl, Jakob Bossek, Daniel Horn, & Michel Lang. (2016). mlrMBO: A Toolbox for Model-Based Optimization of Expensive Black-Box Functions. 4 indexed citations

15.

Richter, Jakob, et al.. (2016). Stallion sperm survival is increased after colloid centrifugation compared to sperm washing, or extension only, in INRA82 or INRA96. Journal of Equine Veterinary Science. 43. S78–S79. 1 indexed citations

16.

Bischl, Bernd, Michel Lang, Jakob Richter, et al.. (2015). Machine Learning in R. 7 indexed citations

17.

Morrell, Jane M., Jakob Richter, Gunilla Martinsson, et al.. (2014). Pregnancy rates after artificial insemination with cooled stallion spermatozoa either with or without Single Layer Centrifugation. Theriogenology. 82(8). 1102–1105. 37 indexed citations

18.

Bischl, Bernd, Michel Lang, Lars Kotthoff, et al.. (2013). mlr: Machine Learning in R. Journal of Machine Learning Research. 17(1). 5938–5942. 200 indexed citations

19.

Höller, H., et al.. (1988). Nettoresorption von Calcium und von anorganischem Phosphat aus dem perfundierten Colon und Rectum von Schafen¹. Journal of Animal Physiology and Animal Nutrition. 59(1-5). 9–15. 7 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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