Jonathan G. Richens

860 total citations · 1 hit paper
10 papers, 482 citations indexed

About

Jonathan G. Richens is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics and Statistical and Nonlinear Physics. According to data from OpenAlex, Jonathan G. Richens has authored 10 papers receiving a total of 482 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Atomic and Molecular Physics, and Optics and 2 papers in Statistical and Nonlinear Physics. Recurrent topics in Jonathan G. Richens's work include Advanced Thermodynamics and Statistical Mechanics (2 papers), Bayesian Modeling and Causal Inference (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Jonathan G. Richens is often cited by papers focused on Advanced Thermodynamics and Statistical Mechanics (2 papers), Bayesian Modeling and Causal Inference (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Jonathan G. Richens collaborates with scholars based in United Kingdom, Italy and Ireland. Jonathan G. Richens's co-authors include Saurabh Johri, Ciarán M. Lee, N. Lo Gullo, Thomas Busch, Steve Campbell, Lluís Masanes, John H. Selby, Álvaro M. Alhambra, Tom Everitt and Zachary Kenton and has published in prestigious journals such as Physical Review Letters, Nature Communications and Physical Review A.

In The Last Decade

Jonathan G. Richens

10 papers receiving 467 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.

Improving the accuracy of medical diagnosis with causal machine learning

2020 355 citations Jonathan G. Richens, Ciarán M. Lee et al. Nature Communications profile →

Peers

Countries citing papers authored by Jonathan G. Richens

Since Specialization

Citations

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

Fields of papers citing papers by Jonathan G. Richens

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan G. Richens. 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 Jonathan G. Richens. The network helps show where Jonathan G. Richens may publish in the future.

Co-authorship network of co-authors of Jonathan G. Richens

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

All Works

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10 of 10 papers shown

#	Work	Indexed citations
1	Discovering agents 2023 ·Artificial Intelligence ·Zachary Kenton, Ramana Kumar, Sebastian Farquhar, Jonathan G. Richens, (unknown), Tom Everitt	9
2	Leveraging Directed Causal Discovery to Detect Latent Common Causes in Cause-Effect Pairs 2022 ·IEEE Transactions on Neural Networks and Learning Systems ·(unknown), Christopher Hart, Jonathan G. Richens, Saurabh Johri	5
3	Author Correction: Improving the accuracy of medical diagnosis with causal machine learning 2021 ·Nature Communications ·Jonathan G. Richens, Ciarán M. Lee, Saurabh Johri	6
4	Improving the accuracy of medical diagnosis with causal machine learning breakdown → 2020 ·Nature Communications ·Jonathan G. Richens, Ciarán M. Lee, Saurabh Johri	355
5	Finite-bath corrections to the second law of thermodynamics 2018 ·Physical review. E ·Jonathan G. Richens, Álvaro M. Alhambra, Lluís Masanes	14
6	Entanglement is necessary for emergent classicality 2017 ·arXiv (Cornell University) ·Jonathan G. Richens, John H. Selby, (unknown)	1
7	Entanglement is Necessary for Emergent Classicality in All Physical Theories 2017 ·Physical Review Letters ·Jonathan G. Richens, John H. Selby, (unknown)	20
8	Work extraction from quantum systems with bounded fluctuations in work 2016 ·Nature Communications ·Jonathan G. Richens, Lluís Masanes	15
9	Reversibility and the structure of the local state space 2015 ·New Journal of Physics ·(unknown), Jonathan G. Richens	6
10	Criticality, factorization, and long-range correlations in the anisotropicXYmodel 2013 ·Physical Review A ·Steve Campbell, Jonathan G. Richens, N. Lo Gullo, Thomas Busch	51

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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