Joris M. Mooij

8.7k total citations · 1 hit paper
47 papers, 3.4k citations indexed

About

Joris M. Mooij is a scholar working on Artificial Intelligence, Molecular Biology and Computer Networks and Communications. According to data from OpenAlex, Joris M. Mooij has authored 47 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 9 papers in Molecular Biology and 8 papers in Computer Networks and Communications. Recurrent topics in Joris M. Mooij's work include Bayesian Modeling and Causal Inference (37 papers), Machine Learning and Algorithms (9 papers) and Error Correcting Code Techniques (8 papers). Joris M. Mooij is often cited by papers focused on Bayesian Modeling and Causal Inference (37 papers), Machine Learning and Algorithms (9 papers) and Error Correcting Code Techniques (8 papers). Joris M. Mooij collaborates with scholars based in Netherlands, Germany and Switzerland. Joris M. Mooij's co-authors include Tom Heskes, Christiaan de Leeuw, Daniëlle Posthuma, Dominik Janzing, Bernhard Schölkopf, Jonas Peters, Hilbert J. Kappen, Patrik O. Hoyer, Jakob Zscheischler and Kun Zhang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and IEEE Transactions on Information Theory.

In The Last Decade

Joris M. Mooij

43 papers receiving 3.3k citations

Hit Papers

align trajectories sort by overperformance

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.

MAGMA: Generalized Gene-Set Analysis of GWAS Data

2015 1.6k citations Joris M. Mooij, Tom Heskes et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Joris M. Mooij Netherlands	20	1.3k	933	933	284	204	47	3.4k
Tom Heskes Netherlands	37	1.7k 1.3×	1.2k 1.3×	1.1k 1.2×	283 1.0×	317 1.6×	230	5.9k
Guido Sanguinetti United Kingdom	37	488 0.4×	2.6k 2.7×	560 0.6×	74 0.3×	123 0.6×	135	4.2k
Carey E. Priebe United States	34	1.4k 1.1×	409 0.4×	205 0.2×	407 1.4×	236 1.2×	217	3.7k
Robert Tibshirani United States	5	1.1k 0.9×	1.5k 1.6×	291 0.3×	1.1k 3.8×	247 1.2×	6	4.8k
Murali Ramanathan United States	46	319 0.3×	2.0k 2.1×	248 0.3×	130 0.5×	96 0.5×	317	7.5k
Carlo Berzuini Italy	28	602 0.5×	497 0.5×	235 0.3×	238 0.8×	66 0.3×	78	2.8k
Alexander J. Hartemink United States	33	618 0.5×	3.5k 3.8×	695 0.7×	97 0.3×	90 0.4×	65	4.8k
Paul S. Bradley United Kingdom	42	1.0k 0.8×	212 0.2×	297 0.3×	81 0.3×	406 2.0×	85	7.8k
Concha Bielza Spain	32	1.8k 1.4×	789 0.8×	87 0.1×	142 0.5×	190 0.9×	180	4.4k
Cheng Soon Ong Australia	24	1.5k 1.2×	963 1.0×	149 0.2×	65 0.2×	191 0.9×	66	4.2k

Countries citing papers authored by Joris M. Mooij

Since Specialization

Citations

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

Fields of papers citing papers by Joris M. Mooij

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joris M. Mooij. 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 Joris M. Mooij. The network helps show where Joris M. Mooij may publish in the future.

Co-authorship network of co-authors of Joris M. Mooij

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

All Works

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20 of 20 papers shown

Mooij, Joris M., et al.. (2023). Causality and independence in perfectly adapted dynamical systems. SHILAP Revista de lepidopterología. 11(1).

Mooij, Joris M., Sara Magliacane, & Tom Claassen. (2020). Joint Causal Inference from Multiple Contexts. Journal of Machine Learning Research. 21(99). 1–108. 28 indexed citations

Mooij, Joris M. & Tom Claassen. (2020). Constraint-Based Causal Discovery with Partial Ancestral Graphs in the presence of Cycles. UvA-DARE (University of Amsterdam). 124. 1159–1168. 1 indexed citations

Mooij, Joris M. & Tom Claassen. (2020). Constraint-Based Causal Discovery In The Presence Of Cycles. arXiv (Cornell University). 1 indexed citations

Mooij, Joris M., et al.. (2017). Algebraic Equivalence Class Selection for Linear Structural Equation Models.. Uncertainty in Artificial Intelligence. 2 indexed citations

Meinshausen, Nicolai, et al.. (2016). Methods for causal inference from gene perturbation experiments and validation. Proceedings of the National Academy of Sciences. 113(27). 7361–7368. 72 indexed citations

Peters, Jonas, et al.. (2016). Theoretical Aspects of Cyclic Structural Causal Models. arXiv (Cornell University). 2 indexed citations

Peters, Jonas, Joris M. Mooij, Dominik Janzing, & Bernhard Schölkopf. (2014). Causal discovery with continuous additive noise models. Journal of Machine Learning Research. 15(1). 2009–2053. 105 indexed citations

Claassen, Tom, Joris M. Mooij, & Tom Heskes. (2014). Supplement - Learning Sparse Causal Models is not NP-hard. Radboud Repository (Radboud University).

10.

Janzing, Dominik, Joris M. Mooij, Kun Zhang, et al.. (2012). Information-geometric approach to inferring causal directions. Artificial Intelligence. 182-183. 1–31. 137 indexed citations

11.

Janzing, Dominik, Jonas Peters, Eleni Sgouritsa, et al.. (2012). On causal and anticausal learning. International Conference on Machine Learning. 459–466. 104 indexed citations

12.

Mooij, Joris M., Dominik Janzing, Tom Heskes, & Bernhard Schölkopf. (2011). On Causal Discovery with Cyclic Additive Noise Models. MPG.PuRe (Max Planck Society). 24. 639–647. 33 indexed citations

13.

Mooij, Joris M.. (2010). libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models. Journal of Machine Learning Research. 11(74). 2169–2173. 160 indexed citations

14.

Stegle, Oliver, Dominik Janzing, Kun Zhang, Joris M. Mooij, & Bernhard Schölkopf. (2010). Probabilistic latent variable models for distinguishing between cause and effect. Neural Information Processing Systems. 23. 1687–1695. 44 indexed citations

15.

Hoyer, Patrik O., Dominik Janzing, Joris M. Mooij, Jonas Peters, & Bernhard Schölkopf. (2008). Nonlinear causal discovery with additive noise models. Max Planck Institute for Plasma Physics. 21. 689–696. 329 indexed citations

16.

Mooij, Joris M. & Dominik Janzing. (2008). Distinguishing between cause and effect. MPG.PuRe (Max Planck Society). 147–156. 18 indexed citations

17.

Gómez, Vicenç, Joris M. Mooij, & Hilbert J. Kappen. (2007). Truncating the Loop Series Expansion for Belief Propagation. Journal of Machine Learning Research. 8(68). 1987–2016. 11 indexed citations

18.

Mooij, Joris M. & Hilbert J. Kappen. (2007). Loop Corrections for Approximate Inference on Factor Graphs. Journal of Machine Learning Research. 8(40). 1113–1143. 8 indexed citations

19.

Mooij, Joris M., et al.. (2007). Loop corrected belief propagation.. Data Archiving and Networked Services (DANS). 331–338. 11 indexed citations

20.

Mooij, Joris M. & Hilbert J. Kappen. (2004). Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks. Radboud Repository (Radboud University). 17. 945–952. 9 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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